The Intelligent Optimization Method And Its Application In Copper Flash Smelting Process

Copper flash smelting is the main metallurgy technique to smelt copper.It is a very complex process with physical and chemical reactions of high temperature and multi-composition,which has such characteristics as multivariable,nonlinearity,strong coupling,large inertia,time varying and uncertainty.Many parameters in the process of copper flash smelting cannot be measured.It is almost impossible to build exactly mathematical model to optimize the process of copper flash smelting.At present,it is difficult to make technique indexes of the process of copper flash smelting steady and optimal in a long time because most of operational parameters are determined by human experience.So the research on intelligent optimization methods for operational patterns in the process of copper flash smelting is of very grand significance to the energy and material consumption reduction,the improvement of usage efficency of resource and equipment,the increment of production ability and technique indexes,and the continuous development of smelting enterprise.In this paper,intelligent optimization method of operational patterns is presented based on the mechanics of complex copper flash smelting. Firstly,this paper introduces the mechanics and technology of copper flash smelting in detail.Secondly,based on generalization of the characteristics and research conditions of operational parameters optimization in the process of copper flash smelting,the intelligent optimization method of operational patterns is suggested.This method mainly includes the optimization of operational patterns and the soft sensing of three parameters which are matte grade,matte temperature and the ratio of iron and silicon in slag.This method has been applied in the process of complex copper flash smelting successfully,and some excellent results were achieved.In this paper,the main results are as follows.(1)The framework of operational optimization control in the copper flash smelting process is presented in this paper.The framework includes the model of soft sensing,work condition estimation,the model of mechanics,the operational pattern optimization and coordination strategy.In the framework,firstly,the three qualitative indexs which are matte grade,matte temperature and the ratio of iron and silicon in slag are measured by use of the model of sofe sensing;and secondly the result of soft sensing is judged by use of the work condition estimation. If the work condition estimation is not well,the framework strats the model of mechanics and the model of operational pattern optimization. Through the coordination strategy,the results of two prats get synthetical output.If the work condition estimation is well,the operational parameters are not changed.The framework lays a foundation for the copper flash smelting intelligent optimization control system.(2)An improved dynamic T-S recurrent fuzzy neural network (DTRFNN)is presented in this paper to soft sensing,for the man-made obtain of the matte grade copper,matte temperature,and the ratio of iron and silicon in slag lags the smelting process.After the structure of DTRFNN is established,the parameters adjustment of BP algorithm,the convergence proof of DTRFNN,and the improvement of the local minima are given in the paper.The three parameters' soft sensing can be settled very well with the improved DTRFNN and the accuracy of the output can get 97%.(3)In the operational patterns intelligent optimization,this paper gives the definitions of the operation patterns and the operation patterns optimization.Based on this,the method of operation patterns decomposition is given.Firstly the operational parameters sets are decomposed by the fuzzy clustering,and secondly the space of optimizational samples is dwindled by the method of similar patterns fusion.Then we can solve the compution burden promble.In the operational patterns optimization algorithm,by profound research of particle swarm algorithm and genetic algorithm,the GARPSO algorithm is presented which includes two parts:an adaptive scheme in particle swarm algorithmis is adopted in the first part to adjust the magnitude of the velocity resiliently,and the promble of premature convergence in particle swarm algorithm is avoided effectively.The second part, combining with genetic algorithm,a resilient particle swarm optimization algorithm to mimic the mature phenomenon is used in combination with genetic algorithm,and the individuals get the greater improvement than before.Through three steps of improvement, crossover,mutation,the GARPSO algorithm can get the optimum solution and supply the support for operational patterns optimization.(4)This paper develops the copper flash smelting intelligent optimization system.This system implements process status visual monitor,three parameters soft sensing and operation parameters optimization,data-base management,data printing,reserving and helping, and data collection and communication.The application of this system implements the energy conservation and stables the productive process.When the matte grade is probably same,the synthesis cost can save 1.2%-1.5%and the applied result is well.