ОПТИМІЗАЦІЯ КИСНЕВО-КОНВЕРТЕРНОГО ПРОЦЕСУ ЗАСОБАМИ АЛГОРИТМІВ МАШИННОГО НАВЧАННЯ

Abstract

Traditional methods for optimising the BOF process in steelmaking are based on experience and rules of thumb, but do not always achieve maximum efficiency. As part of the Industry 4.0 concept, the use of machine learning algorithms is becoming a promising way to optimise production processes. They allow you to analyse large amounts of data collected by a variety of different sensors that operate during an industrial process and find complex relationships between parameters to achieve the best results. The created models can work automatically, analysing data in real time and responding to changes in the process. The use of machine learning algorithms can improve the accuracy of results and the quality of steel production by optimising the BOF process with a large number of parameters and their interrelationships.