Abstract
The main objective of the work is evaluation of effectiveness of the dynamic neural networks in modelling of the copper flash smelting process. The fundamentals of the dynamic neural networks are presented in the paper. This type of neural networks was tested in solving the theoretical problem with time-lag. Next, the dynamic neural networks were applied to prediction of the chosen output parameters of the copper flash smelting process. The copper flash smelting process is very complex and there are many input and output parameters which should be consider in modelling and control of the process. Some of the output process parameters are dependent on the history of the changes of the input parameters. Moreover, some parameters can react to the changes of input parameters with delay, but the values of delays are unknown. This situation causes many problems in modelling of this metallurgical process. The work presents the comparison of the results obtained by dynamic and static neural networks in prediction of the temperature of exhaust gases. The obtained results confirm that the dynamic neural network model can predict output parameters of the copper flash smelting process with high accuracy. Moreover, the dynamic neural networks give the possibility to identify the delays in reaction of the output process parameters to the changes of the input parameters. The obtained results has shown that dynamic neural networks are a very useful tool in modelling of complex metallurgical processes.