| Mineral resources play a very important role in the economic and social development.In our country,mineral resources are rich,but the large population,leads to large demand for mineral resources.Therefore,it is of great significance to improve the utilization of mineral resources to ensure the development of the national economy and to realize the national strategy of sustainable development.Hydrometallurgy is a metallurgical process that uses chemical reactions to extract and separate useful metals from leachate.Hydrometallurgy has several significant advantages.It has high comprehensive recovery rate of valuable metals in the mineral and relatively simple process equipment.Furthermore,it is more environmental-friendly than pyrometallurgy,and its production process is easier to automate.Therefore,hydrometallurgy has been widely used.As the core operating unit of hydrometallurgy,the leaching process directly affects the quality of the subsequent processes.Although the leaching technology in Chinese hydrometallurgy has reached world's advanced level,the control of leaching production process is still at the level of manual control and experienced adjustment,which results in low utilization rate of ore resources.This thesis focuses on the production process of hydrometallurgical in a gold smelter.Based on the in-depth analysis of the characteristics of hydrometallurgy leaching process,a prediction model of the leaching rate is established using the idea of just-in-time learning and the method of least squares support vector machine(LSSVM).Finally,a comprehensive and systematic study on the optimum control of leaching process is carried out.The main work of this thesis is as following:1.Based on the detailed analysis of the reaction mechanism of leaching process,a dynamic mechanism model is established,which consists of the mass conservation equations of gold in the ore,gold in the liquid and cyanide ion in the liquid,as well as the corresponding kinetic reaction rate models of gold and cyanide ion.Moreover,the effect of key variables on the leaching rate is analyzed through simulation,which has laid an important foundation for the predictive model.2.An improved just-in-time learning algorithm is proposed:time order is introduced into selection rule of just-in-time learning set for the determination of modeling neighborhood of current operating point,so as to improve the modelling accuracy;a cumulative similarity factor is adopted to improve real-time performance of the model;the model error is used to take online corrections to the model of the current operating point;the proposed method is applied to predict the leaching rate in the leaching process of hydrometallurgy,and the simulation results shows that the prediction model has high accuracy and good real-time performance.3.After analyzing the demand for optimal control of leaching industrial processes,the optimization model of the leaching process with the economic benefit as the optimization objective and the sodium cyanide dosage as the decision variable is established.To get the steady-state optimization goals,an improved particle swarm optimization(PSO)algorithm is used to solve the optimization model;the optimized leaching rate and sodium cyanide dosage are taken as control set value and control variable initial value,and nonlinear model predictive control method based on standard differential evolution algorithm and improved differential evolution algorithm were used to control the leaching process separately,then from two aspects of tracking performance and anti-jamming performance,the advantages and disadvantages of the two control methods are compared.Simulation experiments show that the nonlinear predictive control method based on improved differential evolution algorithm has better control effect.Finally,the main work of the paper is summarized.And the direction of further research on modeling and optimization control of hydrometallurgical leaching process is discussed and prospected. |
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