Possibilities of an artificial neural network use to control oxygen consumption in a converter shop

Abstract

Converter steelmaking process uses high-performance processing plants and a high level of process automation. Complex multi-stage processes typically use many factors that have effect both on individual stages of the processes and on how a combination of the processes runs as a whole. This makes it necessary to select and/or develop control methods for the converter process having no full current data on process parameters and effects of random disturbances. Use of artificial neural networks is the most effective method available now for analyzing and predicting parameters of different processes (including converter process). This article deals with an algorithm for oxygen measurement based on chemical analysis of steel and process history through some period, using an artificial neural network and machine learning. Initial data were reviewed using correlation and regression analysis, the artificial neural network architecture chosen is reasoned as the most appropriate for the objective on hand and the results are analyzed. The element measurement accuracy achieved is satisfactory, which makes the resulting artificial neural network usable in an automation system for testing purposes. Network performance can be improved with the help of experts in the subject area, whose expertise can be used to correct the model.