Track: Automation and Control

Abstract

Within the processing of sulphide ores, there are problems related to the quality of products and the efficient use of technological equipment. Usually, such issues are resolved due to the engineering experiences and based on mathematical modeling of processes. The mathematical model for optimizing such operating mode is a very difficult program. Performing calculations is required a fairly large investment of time and resources. In our case, the program of the mathematical model for optimizing the operating mode of the processing equipment’s for sinter firing was replaced with a neural network by implementing the process of training the neural networks. The results obtained showed that technologies based on the neural network models given a more accurate and adequate results than mathematical models, which made it possible to solve processing of ores optimization problems of great complexity. The use of neural networks for modeling technological processes has made it possible to increase the efficiency of product quality control systems and automatic control systems for the firing of sulfide ores.