(untitled) (MCIT: Proceedings of International scientific and practical conference, 2023)
The article is aimed at the development of modern automatic control systems, which should provide high-performance indicators in the conditions of variable operating modes of industrial equipment due to effective control structures and algorithms. The purpose of the study is to reduce the cost of the basic oxygen furnace steel, which is a consequence of the increase in the share of scrap metal due to the enhanced post-burning of CO to CO2 in the cavity, by optimal controlling the duty mode parameters using model-predictive control. The blowing mode of the basic oxygen furnace was considered as a technological object of control, and the problem of controlling blowing parameters in conditions of non-stationarity of the rate of metal decarburization was analyzed. The use of a model-predictive controller made it possible to improve the quality of control for the oxygen flow circuit by 39% and the maximum dynamic deviation of the CO2 content in the gases was reduced by 16.5% compared to the PID control. The implementation of a software-hardware control system using model-predictive control based on a programmable logic controller is considered.