Abstract
Silicon content ([Si]) of the molten metal is an important index reflecting the product quality and thermal status of the whole blast furnace (BF) ironmaking process. Since the direct online measure on this index is difficult and larger time lag exists in the offline assay procedure, quality modeling is required to achieve online estimation of [Si], which is an open problem for realizing BF automation. Focusing on this practical problem, this paper proposes a data-driven dynamic modeling method for [Si] prediction using extreme learning machine (ELM) with the help of principle component analysis (PCA). First, data-driven PCA is introduced to pick out the most pivotal variables from multitudinous factors that influence [Si] to serve as the secondary variables of modeling. Second, since this BF metallurgical process is nonlinearity dynamic system with severe time-varying characteristic, dynamic ELM modeling technology with good generalization performance and strong nonlinear mapping capability is proposed by applying the self-feedback structure on traditional ELM. The self-feedback connection enables ELM to overcome the static mapping limitation of its feedforward network structure so as can cope with dynamic time-series prediction problems very well. At last, industrial experiments and compared studies demonstrate that the constructed model has a better modeling and estimating accuracy as well as a faster learning speed when compared with different modeling method and different model structure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jian, L., Gao, C.H., Xia, Z.H.: Constructing multiple kernel learning framework for blast furnace automation. IEEE Transactions on Automation Science and Engineering 9(4), 763–777 (2012)
Gao, C.H., Jian, L., Luo, S.H.: Modeling of the thermal state change of blast furnace hearth with support vector machines. IEEE Transactions on Industrial Electronics 59(2), 1134–1145 (2012)
Brik, W., Marklund, O., Medvedev, A.: Video monitoring of pulverized coal injection in the blast furnace. IEEE Transactions on Industrial Applications 38(2), 571–576 (2002)
Saxen, H., Gao, C.H., Gao, Z.W.: Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace-A review. IEEE Transactions on Industrial Informatics 9(4), 2213–2225 (2013)
Ueda, S., Natsui, S., Nogami, H., Yagi, J., Ariyama, T.: Recent progress and future perspective on mathematical modeling of blast furnace. Int. ISIJ 50(7), 914–923 (2010)
Phadke, M., Wu, S.M.: Identification of multi input-multi output transfer function and noise model of a blast furnace from closed-loop data. IEEE Transactions on Automatic Control 19(6), 944–951 (1974)
Castore, M., Gandolfi, G., Palella, S., Taspedini, G.: Dynamic model for hot-metal Si prediction in blast-furnace control. In: Proc. Developments Ironmaking Practice, Iron and Steel Institute, London, U.K., pp. 152–159 (1972)
Chao, Y.C., Su, C.W., Huang, H.P.: The adaptive autoregressive models for the system dynamics and prediction of blast-furnace. Chem. Eng. Commun. 44, 309–330 (1986)
Bhattacharaya, T.: Prediction of silicon content in blast furnace hot metal using partial least squares (PLS). Int. ISIJ 45(1), 1943–1945 (2005)
Jiménez, J., Mochón, J., de Ayala, J.S., Obeso, F.: Blast furnace hot metal temperature prediction through neural networks-based models. Int. ISIJ 44, 573–580 (2004)
Saxén, H., Pettersson, F.: Nonlinear prediction of the hot metal silicon content in the blast furnace. Int. ISIJ 47(12), 1732–1737 (2007)
Chen, J.: A predictive system for blast furnaces by integrating a neural network with qualitative analysis. Engineering Applications of Artificial Intelligence 14(1), 77–85 (2001)
Tang, X., Zhuang, L., Jiang, C.: Prediction of silicon content in hot metal using support vector regression based on chaos particle swarm optimization. Expert Syst. Appl. 36(9), 11853–11857 (2009)
Gao, C.H., Ge, Q.H., Jian, L.: Rule extraction from fuzzy-based blast furnace SVM multiclassifier for decision-making. IEEE Transactions on Fuzzy Systems 22(3), 586–596 (2014)
Liang, N.Y., Huang, G.B., Saratchandran, P., Sundararajan, N.: A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks 17(6), 1411–1423 (2006)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference Neural Networks, vol. 2, pp. 985–990 (2004)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: Theory and applications. Neurocomputing 70(1-3), 489–501 (2006)
Huang, G.B., Babri, H.A.: Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Trans. on Neural Networks 9(1), 224–229 (1998)
Huang, G.B., Zhou, H.M., Ding, X.J., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(2), 513–529 (2012)
Lan, Y., Soh, Y.C., Huang, G.B.: Ensemble of online sequential extreme learning machine. Neurocomputing 72(13-15), 3391–3395 (2009)
Moreno, R., Corona, F., Lendasse, A., Graña, M., Galvão, S.: Extreme learning machines for soybean classification in remote sensing hyperspectral images. Neurocomputing 128, 207–216 (2014)
Sun, Y.J., Yuan, Y., Wang, G.R.: Extreme learning machine for classification over uncertain data. Neurocomputing 128, 500–506 (2014)
Savitha, R., Suresh, S., Sundararajan, N.: Fast learning circular complex-valued extreme learning machine (CC-ELM) for real-valued classification problems. Information Sciences 187, 277–290 (2012)
Yu, Q., Miche, Y., Séverin, E., Lendasse, A.: Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing 128, 296–302 (2014)
Wang, G.R., Zhao, Y., Wang, D.: A protein secondary structure prediction framework based on the extreme learning machine. Neurocomputing 72(1-3), 262–268 (2008)
Zhang, J., Martin, E., Morris, A.J.: Fault detection and classification through multivariate statistical techniques. In: Proceedings of American Control Conference, vol. 1, pp. 751–755 (1995)
Good, R.P., Kost, D., Cherry, G.A.: Introducing a unified PCA algorithm for model size reduction. IEEE Transactions on Semiconductor Manufacturing 23(2), 201–209 (2010)
Zhao, J., Wang, W., Liu, Y., Pedrycz, W.: A two-stage online prediction method for a blast furnace gas system and its application. IEEE Transactions on Control Systems Technology 19(3), 507–520 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhou, P., Yuan, M., Wang, H. (2015). ELM Based Dynamic Modeling for Online Prediction of Molten Iron Silicon Content in Blast Furnace. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-14066-7_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14065-0
Online ISBN: 978-3-319-14066-7
eBook Packages: EngineeringEngineering (R0)