Model predictive control application in the energy saving technology of basic oxygen furnace

Authors

  • Yurii Mariiash National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»
  • Oleksandr Stepanets Department of Automation of heat-and-power engineering processes National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv

DOI:

https://doi.org/10.31713/MCIT.2019.33

Keywords:

Model Predictive Control, Basic Oxygen Furnace, Predictive Model, Linear-Quadratic Functional, Optimal Control, Energy Saving

Abstract

In today's conditions of development, maximization of profitability and ensuring technological safety of metallurgical production, the tasks of developing resource-saving technological modes of steel smelting are urgent. The fulfilment of the condition for the simultaneous achievement of the desired chemical composition and temperature of the metal is ensured by controlling the oxygen consumption and the position of the oxygen impeller lance, therefore solving the problem of managing the blasting of the converter bath is the main task.

The mathematical modelling of the bath purge during steel smelting for energy-saving technology is executed. The method for solving Model Predictive Control with quadratic functionality in the presence of constraints is given. The algorithms of work aimed at reliability of equipment operation and energy saving are introduced.

Implementation of the described solutions will contribute to improving efficiency and reliability of equipment, increasing the proportion of scrap and reducing the melting period without changing of technological process.

 

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Published

2019-11-05

How to Cite

Mariiash, Y., & Stepanets, O. (2019). Model predictive control application in the energy saving technology of basic oxygen furnace. Modeling, Control and Information Technologies: Proceedings of International Scientific and Practical Conference, (3), 124–126. https://doi.org/10.31713/MCIT.2019.33

Issue

Section

Problems of automated control, optimization and parameters identification

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