MODEL PREDICTIVE CONTROL APPLICATION IN THE ENERGY SAVING TECHNOLOGY OF BASIC OXYGEN FURNACE
Oleksandr Stepanets
stepanets.av@gmail.comNational Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Automation of Heat and Power Engineering Processes (Ukraine)
http://orcid.org/0000-0003-4444-0705
Yurii Mariiash
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Automation of Heat and Power Engineering Processes (Ukraine)
http://orcid.org/0000-0002-0812-8960
Abstract
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. The method for solving Model Predictive Control with quadratic functionality in the presence of constraints is given. Implementation of the described solutions will contribute to increasing the proportion of scrap and reducing the melting period without changing of technological process.
Keywords:
model predictive control, basic oxygen furnace, optimal control, energy savingReferences
Backman J., et al.: Methods and Tools of Improving Steel Manufacturing Processes: Current State and Future Methods. International Federation of Automatic Control PapersOnLine 52(13)/2019, 1174–1179.
DOI: https://doi.org/10.1016/j.ifacol.2019.11.355
Google Scholar
Bogushevskiy V.S., et al.: System for the BOF Process Control. The Advanced Science Open Access Journal 5/2013, 23–27.
Google Scholar
Bogushevskiy V.S., Zuboka C.M.: Mathematical modeling of the converter process by energy-saving technology. Technological complexes 2/2013, 32–38.
Google Scholar
Camacho E.F., Bordons A.: Model Predictive Control. 2nd ed, Springer-Verlag London 2007.
DOI: https://doi.org/10.1007/978-0-85729-398-5
Google Scholar
Cherneha D.F., et al.: Fundamentals of metallurgical production of metals and alloys. High School, Kyiv 2006.
Google Scholar
Ghosh S., et al.: BOF process dynamics. Mineral Processing and Extractive Metallurgy 128(1)/2018, 1–17.
DOI: https://doi.org/10.1080/25726641.2018.1544331
Google Scholar
Kouvaritakis B., Cannon M.: Model Predictive Control Classical, Robust and Stochastic. Springer-Verlag, London 2016.
DOI: https://doi.org/10.1007/978-1-4471-5058-9_7
Google Scholar
Ruuska J., et al.: Mass-balance Based Multivariate Modelling of Basic Oxygen Furnace Used in Steel Industry. International Federation of Automatic Control PapersOnLine 50(1)/2017, 13784–13789.
DOI: https://doi.org/10.1016/j.ifacol.2017.08.2065
Google Scholar
Stepanets O., Mariiash Y.: Analysis of Influence of Technical Features of a PID – controller Implementation on The Dynamics of Automated Control System. Eastern-European Journal of Enterprise Technologies 3(2)/2018, 60–69.
DOI: https://doi.org/10.15587/1729-4061.2018.132229
Google Scholar
Zhang J.: Optimal Control Problem of Converter Steelmaking Production Process Based on Operation Optimization Method. Discrete Dynamics in Nature and Society 2015, Article ID 483674.
DOI: https://doi.org/10.1155/2015/483674
Google Scholar
MathWorks. Design Controller Using MPC Designer. https://www.mathworks.com/help/mpc/gs/introduction.html?ue (available 5.09. 2018).
Google Scholar
Authors
Oleksandr Stepanetsstepanets.av@gmail.com
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Automation of Heat and Power Engineering Processes Ukraine
http://orcid.org/0000-0003-4444-0705
Authors
Yurii MariiashNational Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Automation of Heat and Power Engineering Processes Ukraine
http://orcid.org/0000-0002-0812-8960
Statistics
Abstract views: 306PDF downloads: 197
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.