Application of artificial neural network for determination of the additives amount in the automated process control system of steelmaking in basic oxygen furnace
DOI:
https://doi.org/10.31498/2225-6733.29.2014.39296Keywords:
modeling, artificial neural network, steel industryAbstract
This paper describes an algorithm of determining the amount of deoxidizing and alloying materials that are loaded into the basic oxygen furnace (BOF) and steel ladle on the base of information about burdening of melting and chemical composition of the steel using artificial neural network (ANN). The analysis of resent researches and publications regarding mathematical modeling of BOF melting and application of ANN as such models was made. This analysis show that selected topic has novelty and relevance. The schematic of interaction of different kinds of mathematical models in the system of automated control of BOF melting is offered. The research of applicability of artificial neural networks for determination of quantity of deoxidizing and alloying components is performed. The place of the obtained artificial neural network in the overall system of automated control of basic oxygen melting is described. The description of the multistep selection process of the ANN architecture is given. The correlation coefficients and mean square deviations for all parameters are found. The results of performed analysis are considered satisfactory. The recommendations for replacement of alloying and deoxidizing components in the absence of any of them in stock are givenReferences
Hybrid Modeling of Molten Steel Temperature Prediction in LF / Huixin Tian, Zhizhong Mao, Yan Wang // ISIJ International. – 2008. – №1. – P. 58-62.
A New Incremental Learning Modeling Method Based on Multiple Models for Temperature Prediction of Molten Steel in LF / Huixin Tian, Zhizhong Mao, Yan Wang // ISIJ International. – 2009. – №1. – P. 58-63.
Uma K. Matlab Based Artificial Neural Network Model for Prediction of Melt down Temperature in Steel Making / K. Uma, R.M. Potdar // International Journal of Engineering and Management Sciences. – 2011. – № 2(4). – P. 22-232.
Meradi H. Prediction of Bath Temperature using Neural Networks / H. Meradi, S. Bouhouche, M. Lahreche // World Academy of Science, Engineering and Technology. – 2008. – P. 946-950.
Converter end-point prediction model using spectrum image analysis and improved neural network algorithm / Hong-Yuan Wen, Qi Zhao, Yan-Ru Chen, Mu-Chun Zhou, Meng Zhang, Ling-Fei Xu // Optica Applicata. – 2008. – No. 4. – P. 693-704.
Neural Network-Based Prediction Of Additives In The Steel Refinement Process / Tadeusz Wieczorek, Mirosław Kordos // Computer Methods In Materials Science. – 2010. – №1. – P. 1-10.
Turkenich D.I. Control of steel melting in BOF / D.I. Turkenich. – Moscow : Metallyrgy, 1978. – 360 p. (Rus.)
Mathematical models and systems of control of BOF melting / V.S. Bogushevskiy, L.F. Litvinov, N.A. Ryumshin, V.V. Sorokin. – Kiev : NPK «Kiev Institute of Automation», 1998. – 304 p. (Rus.)
Bigeev A.M. Mathematical description and calculations of steelmaking processes / A.M. Bigeev. – Moscow : Metallurgy, 1982. – 160 p. (Rus.)
Downloads
How to Cite
Issue
Section
License
The journal «Reporter of the Priazovskyi State Technical University. Section: Technical sciences» is published under the CC BY license (Attribution License).
This license allows for the distribution, editing, modification, and use of the work as a basis for derivative works, even for commercial purposes, provided that proper attribution is given. It is the most flexible of all available licenses and is recommended for maximum dissemination and use of non-restricted materials.
Authors who publish in this journal agree to the following terms:
1. Authors retain the copyright of their work and grant the journal the right of first publication under the terms of the Creative Commons Attribution License (CC BY). This license allows others to freely distribute the published work, provided that proper attribution is given to the original authors and the first publication of the work in this journal is acknowledged.
2. Authors are allowed to enter into separate, additional agreements for non-exclusive distribution of the work in the same form as published in this journal (e.g., depositing it in an institutional repository or including it in a monograph), provided that a reference to the first publication in this journal is maintained.
