Endpoint prediction model for basic oxygen furnace steel-making based on membrane algorithm evolving extreme learning machine

Applied Soft Computing Journal

Volumn 19, Issue , 2014, Pages 430-437

  Han, Min ^a   Liu, Chuang ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Basic oxygen furnace; Endpoint carbon content; Endpoint temperature; Evolutionary membrane algorithm; Extreme learning machine; Prediction model; Soft measurement

Indexed keywords

BASIC OXYGEN CONVERTERS; CARBON; COMPUTER SIMULATION; EVOLUTIONARY ALGORITHMS; FORECASTING; GAUSSIAN NOISE (ELECTRONIC); KNOWLEDGE ACQUISITION; LADLE METALLURGY; LEARNING SYSTEMS; MATHEMATICAL MODELS; STEEL FOUNDRY PRACTICE; STEELMAKING;

CARBON CONTENT; END-POINT TEMPERATURE; EXTREME LEARNING MACHINE; PREDICTION MODEL; SOFT MEASUREMENT;

PARAMETER ESTIMATION;

EID: 84899437762     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2013.09.012     Document Type: Article

References (46)

1. R. Fruehan Overview of steelmaking processes and their development The Making, Shaping and Treating of Steel: Steelmaking and Refining 1998
2. M. Han, and Y. Zhao Dynamic control model of BOF steelmaking process based on ANFIS and robust relevance vector machine Expert Systems with Applications 38 12 2011 14786 14798
3. X. Wang, M. Han, and J. Wang Applying input variables selection technique on input weighted support vector machine modeling for BOF endpoint prediction Engineering Applications of Artificial Intelligence 23 6 2010 1012 1018
4. A. Ghosh, and A. Chatterjee Ironmaking and Steelmaking: Theory and Practice 2008 Prentice-Hall of India
5. F. Pettersson, H. Saxen, and K. Deb Genetic algorithm-based multicriteria optimization of ironmaking in the blast furnace Materials and Manufacturing Processes 24 3 2009 343 349
6. H. Saxén, F. Pettersson, and K. Gunturu Evolving nonlinear time-series models of the hot metal silicon content in the blast furnace Materials and Manufacturing Processes 22 5 2007 577 584 (Pubitemid 46863321)
7. F. Pettersson, N. Chakraborti, and H. Saxén A genetic algorithms based multi-objective neural net applied to noisy blast furnace data Applied Soft Computing 7 1 2007 387 397 (Pubitemid 44739441)
8. M. Sayed-Mouchaweh, and E. Lughofer Learning in Non-Stationary Environments: Methods and Applications 2012 Springer New York
9. M. Han, and L. Jiang Endpoint prediction model of basic oxygen furnace steelmaking based on PSO-ICA and RBF neural network 2010 International Conference on Intelligent Control and Information Processing (ICICIP) IEEE 2010 388 393
10. G. Brooks, N. Dogan, M. Alam, J. Naser, and M. Rhamdhani Developments in the modelling of oxygen steelmaking Guthrie Honorary Symposium, 6'C9 June 2011, Montreal, QC, Canada 2011
11. R. Kornblum, and M. Tribus The use of bayesian inference in the design of an endpoint control system for the basic oxygen steel furnace IEEE Transactions on Systems Science and Cybernetics 6 4 1970 339 348
12. P. Chen, and Y.Z. Lu Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction Journal of Zhejiang University Science A 11 11 2010 841 848
13. I. Cox, R. Lewis, R. Ransing, H. Laszczewski, and G. Berni Application of neural computing in basic oxygen steelmaking Journal of Materials Processing Technology 120 1 2002 310 315 (Pubitemid 34069990)
14. A. Cunha, T. Pacianotto, and A. Frattini Fileti Steelmaking process: neural models improve end-point predictions Computer Aided Chemical Engineering 18 2004 631 636
15. A. Fileti, T. Pacianotto, and A.P. Cunha Neural modeling helps the BOS process to achieve aimed end-point conditions in liquid steel Engineering Applications of Artificial Intelligence 19 1 2006 9 17 (Pubitemid 41795691)
16. N. Rajesh, M.R. Khare, and S.K. Pabi Feed forward neural network for prediction of end blow oxygen in LD converter steel making Materials Research 13 1 2010 15 19
17. F. De Medeiros, S. Noblat, and A. Fileti Reviving traditional blast furnace models with new mathematical approach Ironmaking & Steelmaking 34 5 2007 410 414 (Pubitemid 47609255)
18. L.F. Xu, W. Li, M. Zhang, S.X. Xu, and J. Li A model of basic oxygen furnace (BOF) end-point prediction based on spectrum information of the furnace flame with support vector machine (SVM) Optik-International Journal for Light and Electron Optics 122 7 2011 594 598
19. L.F. Xu, W.S. Li, S.X. Xu, J. Li, and Y.Q. Wang A new spectral analysis technique used in converter steelmaking BOF endpoint control Advanced Materials Research 139 2010 689 692
20. 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 Applicata 38 4 2008 693 704
21. Y. Iida Fully automatic blowing technique for basic oxygen steelmaking furnace Transactions of the Iron and Steel Institute of Japan 24 7 1984 540 546 (Pubitemid 14647709)
22. W. Birk, A. Johansson, A. Medvedev, and R. Johansson Model-based estimation of molten metal analysis in the LD converter: experiments at SSAB Tunnplat AB in Lulea IEEE Transactions on Industry Applications 38 2 2002 565 570 (Pubitemid 34491538)
23. B.Y. Cai, H. Zhao, and Y.J. Yue Research on the BOF steelmaking endpoint temperature prediction 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC) IEEE 2011 2278 2281
24. K.X. Peng, J. Zhang, and J. Dong Intelligent method in the end-point control of BOF Applied Mechanics and Materials 20 2010 796 800
25. A. Bouchachia, E. Lughofer, and D. Sanchez Editorial of the special issue: online fuzzy machine learning and data mining Information Sciences 220 2013 1 4
26. P. Angelov, D.P. Filev, and N. Kasabov Evolving Intelligent Systems: Methodology and Applications, vol. 12 2010 Wiley.com
27. E. Lughofer Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications 2011 Springer
28. A. Bouchachia, and N. Nedjah Adaptive incremental learning in neural networks Neurocomputing 74 11 2011 1783 1784
29. J. Gama, P.P. Rodrigues, E.J. Spinosa, and A.C.P.L.F. de Carvalho Knowledge Discovery from Data Streams 2010 Citeseer
30. K. Kasabov, and N. Kasabov Evolving Connectionist Systems: The Knowledge Engineering Approach 2007 Springer
31. G.B. Huang, Q.Y. Zhu, and C.K. Siew Extreme learning machine: theory and applications Neurocomputing 70 1 2006 489 501
32. G. Li, P. Niu, C. Liu, and W. Zhang Enhanced combination modeling method for combustion efficiency in coal-fired boilers Applied Soft Computing 12 10 2012 3123 3140
33. C. Liu, M. Han, and X.Z. Wang A novel evolutionary membrane algorithm for global numerical optimization 2012 Third International Conference on Intelligent Control and Information Processing (ICICIP) IEEE 2012 727 732
34. G. Pǎun Computing with membranes Journal of Computer and System Sciences 61 1 2000 108 143
35. G. Pǎun, G. Rozenberg, and A. Salomaa The Oxford Handbook of Membrane Computing 2010 Oxford University Press, Inc
36. G. Ciobanu, G. Pǎun, and M.J. Pérez-Jiménez Applications of Membrane Computing 2006 Springer
37. R. Caponetto, L. Fortuna, S. Fazzino, and M. Xibilia Chaotic sequences to improve the performance of evolutionary algorithms IEEE Transactions on Evolutionary Computation 7 3 2003 289 304 10.1109/TEVC.2003.810069
38. G.-B. Huang, H. Zhou, X. Ding, and R. Zhang Extreme learning machine for regression and multiclass classification IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42 2 2012 513 529
39. R. Zhang, Y. Lan, G.-B. Huang, and Z.-B. Xu Universal approximation of extreme learning machine with adaptive growth of hidden nodes IEEE Transactions on Neural Networks and Learning Systems 23 2 2012 365 371
40. S. Suresh, R. Venkatesh Babu, and H. Kim No-reference image quality assessment using modified extreme learning machine classifier Applied Soft Computing 9 2 2009 541 552
41. S. Saraswathi, S. Sundaram, N. Sundararajan, M. Zimmermann, and M. Nilsen-Hamilton ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented IEEE/ACM Transactions on Computational Biology and Bioinformatics 8 2 2011 452 463
42. S.S. Kim, and K.C. Kwak Incremental modeling with rough and fine tuning method Applied Soft Computing 11 1 2011 585 591
43. Y. Miche, A. Sorjamaa, P. Bas, O. Simula, C. Jutten, and A. Lendasse OP-ELM: optimally pruned extreme learning machine IEEE Transactions on Neural Networks 21 1 2010 158 162
44. G.B. Huang, L. Chen, and C.K. Siew Universal approximation using incremental constructive feedforward networks with random hidden nodes IEEE Transactions on Neural Networks 17 4 2006 879 892 (Pubitemid 44194149)
45. N. Liu, and H. Wang Weighted principal component extraction with genetic algorithms Applied Soft Computing 12 2 2012 961 974
46. Q.Y. Zhu, A.K. Qin, P.N. Suganthan, and G.B. Huang Evolutionary extreme learning machine Pattern Recognition 38 10 2005 1759 1763 (Pubitemid 41037107)