Optimization decision system for operational parameters in the process of matter converting

An optimization decision system for operational parameters in the process of matter converting is developed. In this paper, the offline optimization decision model for blustering parameters based on neural networks and chaotic genetic algorithm is discussed in detail. A neural network is employed to approximate the function of blustering parameters with state parameters, and an improved chaotic genetic algorithm is used to search the more optimal blustering parameters. At last, the functional modules of the system are listed, and the industrial application is introduced. The practical result shows that the output of converter increases by 6%, and the quantity of cool materials treated rises by 7.8%.