Modeling of BOF Steelmaking Based on the Data-Driven Approach

Abstract:

This paper is concerned with the modeling of both endpoint temperature and carbon content for BOF steelmaking. First, a linear regression predictive model is constructed based on the linear regression analysis method. Next, the response surface analysis method is used to construct a nonlinear predictive model. The significant contribution of this paper is that response surface analysis is proposed for constructing the predictive model of BOF steelmaking. Finally, experiment simulation results show the effectiveness and advantages of the proposed methods.

• Periodical: Advanced Materials Research (Volume 818)
• Pages: 92-97
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.818.92
• Online since: September 2013
• Authors: Jin Na Li, Yuan Li, Fang Liu, Sheng Ping Bie, Cheng Chi Tang, Xiao Ling Zhang
• Keywords: Basic Oxygen Furnace (BOF) Steelmaking, Linear Regression Analysis, Particle Swarm Optimization (PSO), Response Surface Analysis

References

[1] M. Han and Y. Zhao, Dynamic control model of BOF steelmaking process based on ANFIS and robust relevance vector machine, Expert Systems with Applications, vol. 38, no. 12, pp.14786-14798, (2011).

DOI: 10.1016/j.eswa.2011.05.071

[2] S.Y. Yun and K.S. Chang, Dynamic prediction using neural network for automation of BOF process in steel industry, Iron and Steelmaker, vol. 23, no. 8, pp.37-42, (1996).

[3] R. Ding and L. Liu, Artificial intelligence static control model in converter steelmaking, Iron and Steel, vol. 32, no. 1, pp.22-26, (1997).

[4] S.M. Xie, J. Tao, and T.Y. Chai, BOF steelmaking endpoint control based on network, Control Theory and Applications, vol. 20, no. 6, pp.903-907, (2003).

[5] Z.J. Wang, T.Y. Chai, and C. Shao, Slab minerature prediction model based on RBF neural network, Journal of System Simulation, vol. 11, no. 3, pp.181-184, (1999).

[6] S.M. Xie, X.W. Gao, and T.Y. Chai, BOF Endpoint Prediction Based on Grey Model, Journal of Iron AND Steel Research, vol. 11, no. 4, pp.9-12, (1999).

[7] H.Y. Wen, Q. Zhao, Y.R. Chen, M.C. Zhou, M. Zhang, and L.F. Xu, Converter end-point prediction model using spectrum image analysis and improved neural network algorithm, Optica Application, vol. 38, no. 4, pp.693-695, (2008).

[8] X.R. Kong, Research on the endpoint optimization control model of BOF steelmaking, Hangzhou University of Electronic Science and Technology.

[9] S. Ren, Modeling the Toxicity of Aromatic Compounds to Tetrahymena pyriformis: The Response Surface Methodology with Nonlinear Methods, Journal of Chem Inf Comput Sci., vol. 43, no. 5, pp.1679-1687, (2003).

DOI: 10.1021/ci034046y

[10] J.W. Zhuo, Application of MATLAB in mathematical modeling, Beijing：Beijing University of Aeronautics and Astronautics Press, (2011).

[11] R. Liu and C.Y. Lv, The application of adaptively adjustment particle swarm optimization algorithm in catalyzing & cracking fractionating tower，Computers and Applied Chemistry, vol. 27, no. 6, pp.771-774, (2010).

[12] W.L. Yu, Q.X. Wu, and C.S. Jiang, Application of particle swarm algorithm in 3-D route planning and optimization of air vehicles, Electronics Optic & Control, vol. 15, no. 5, pp.1-6, (2008).