Optimization And Control For Hydrometallurgy Leaching Process

The utilization of mineral resources plays a big role in the economic development of countries around the world,and it is the basis for the development of modern industrialization and scientific and technical progress of a number of areas.With the development of the world economy and rapid population growth,the demand for mineral resources by mankind is increasing.In order to ensure the sustainable development of mineral resources,it has become a very serious and urgent task to increase the efficiency of resource development and utilization.Hydrometallurgy is a metallurgical process that utilizes leachate to extract and separate useful metals from minerals.It has a high recovery rate for valuable metals in minerals,and can handle complex ores and low grade minerals with less environmental pollution.Therefore,it has been widely used.The leaching process is a key process in the hydrometallurgical process.At present,the control of the leaching production process in hydrometallurgical plants is mainly achieved manually,and the control level is not high,which affects the product quality and economic benefits.Therefore,in this thesis,the leaching process of a certain hydrometallurgical production plant is taken as the research object.Based on the analysis of the leaching process,the leaching process mechanism model is established,and on this basis,the optimization setting and predictive control of the leaching process are carried out.The main work of this thesis is summarized as follows:1.Based on the analysis of the reaction mechanism in the leaching process,the leaching mechanism models is established,including:the conservation model of gold in ore,the conservation model of gold in the liquid phase,the conservation model of cyanide ion,and the kinetic reaction rate models of gold and cyanide ions.Based on the models,the effects of various variables on the leaching rate of cyanidation leaching have been analyzed.2.Based on the basic concepts of classic differential evolution algorithm and tabu search algorithm,an improved differential evolution algorithm is proposed.Using a set of test functions and standard evaluation indicators,the classic differential evolution algorithm and the improved differential evolution algorithm are compared,and the superiority of the improved differential evolution algorithm has been verified.3.Through the analysis of the leaching process optimization and control requirements,taking the economic benefits as the optimization goal and the feeding amount of sodium cyanide as the decision variable,an optimization model of the leaching process is established and the improved differential evolution algorithm is used to solve the optimization model.On the basis of the optimization results,a non-linear model predictive control method based on an improved particle swarm optimization algorithm is used to control the leaching process.The simulation results in two aspects of tracking performance and disturbance rejection performance indicate that the adopted predictive control method has better control effect than PID control.Finally,the main work of the paper is summarized and the main directions for further study are considered.