Abstract
Hearth activity is one of the most important factors which affect the smooth progress of production and even the life of blast furnace. However, the calculation of hearth activity depends on the empirical model entirely, and the model parameter acquisition is difficult. To overcome this deficiency, this paper presents a novel method based on an improved multiple linear regression model to predict average temperature of core dead stock column for evaluating it. In the algorithm, the Pearson correlation analysis, metallurgical formulas and the Akaike Information Criterion based on least square method are used to establish a multiple linear regression model. The method makes the estimation of hearth activity out of the empirical formula. And it is easy for the evaluated model to obtain parameters. Meanwhile, experimental results show our proposed method can achieve 0.69% average relative error on the test data set and average relative error of 0.57% on the training data set. Moreover, the function of low average temperature of core dead stock column warning can be realized.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhou, D., Cheng, S., Zhang, R., Li, Y., Chen, T.: Uniformity and activity of blast furnace hearth by monitoring flame temperature of raceway zone. ISIJ Int. 57, 1509–1516 (2017)
Luomala, M.J., Mattila, O.J., Härkki, J.J.: Physical modelling of hot metal flow in a blast furnace hearth. Scand. J. Metall. 30, 225–231 (2010)
Shibata, K., Kimura, Y., Shimizu, M., Inaba, S.I.: Dynamics of dead-man coke and hot metal flow in a blast furnace hearth. Revue De Métallurgie 87, 333–340 (2007)
Jiao, K., Zhang, J.L., Liu, Y.X., Li, S.F., Liu, F.: Analysis on the stamping coke dissolution of hot metal in the blast furnace hearth. Can. Metall. Q. 56, 1–7 (2017)
Gao, C., Jian, L., Luo, S.: Modeling of the thermal state change of blast furnace hearth with support vector machines. IEEE Trans. Industr. Electron. 59, 1134–1145 (2011)
Zhang, Y., Deshpande, R., Huang, D., Chaubal, P., Zhou, C.Q.: Numerical analysis of blast furnace hearth inner profile by using CFD and heat transfer model for different time periods. Int. J. Heat Mass Transf. 51, 186–197 (2014)
Zolotykh, M.O., Dmitriev, A.N., Vitkina, G.Y.: The association of various approaches to the monitoring of lining condition in the blast furnace hearth. Defect Diffus. Forum 380, 186–190 (2017)
Gomes, F.S.V., Coco, K.F., Salles, J.L.F.: Multistep forecasting models of the liquid level in a blast furnace hearth. IEEE Trans. Autom. Sci. Eng. 14, 1286–1296 (2017)
Komiyama, K.M., Guo, B.Y., Zughbi, H., Zulli, P., Yu, A.B.: Improved CFD model to predict flow and temperature distributions in a blast furnace hearth. Metall. Mater. Trans. B 45, 1895–1914 (2014)
Agrawal, A., Kor, S.C., Nandy, U., Choudhary, A.R., Tripathi, V.R.: Real-time blast furnace hearth liquid level monitoring system. Ironmaking Steelmaking 43, 160128032747001 (2016)
Dmitriev, A.N., Zolotykh, M.O., Chen, K., Vitkina, G.Y.: The thermophysical bases of monitoring of the fireproof lining wear in the blast furnace hearth. Defect Diffus. Forum 370, 113–119 (2017)
Raipala, K.: Deadman and hearth phenomena in the blast furnace. Scand. J. Metall. 29, 39–46 (2010)
Chen, H., Wu, S.L., Yu, X.B.: New index of evaluating activity of blast furnace hearth. Iron Steel 42, 12–15 (2007)
Dai, B., Liang, K., Wang, X.J., Xin, L.I., Guo, Y.W., Works, I.: Development and practice of quantitative calculation models of blast furnace hearth activity. China Metallurgy (2015)
Chen, C., Gang, A.N., Plant, I.: Activity index and improvement measure of BF hearth. Iron Steel 29–33 (2018)
Jin, J.: Practice of active hearth condition during long-term production with High PCR. Bao Steel Technol. 13–16 (2002)
Neto, A.M., Rittner, L., Leite, N., Zampieri, D.E.: Pearson’s correlation coefficient for discarding redundant information in real time autonomous navigation system. In: IEEE International Conference on Control Applications, pp. 50–19 (2007)
Liu, C., Jin, R., Gong, E., Liu, Y., Yue, M.: Prediction for the performance of gas turbine units using multiple linear regression. Proc. CSEE 37, 4731–4738 (2017)
Zhou, H.Y.: On oxygen enrichment blast in front of blower. Enterprise Sci. Technol. Dev. (2012)
Arnold, T.W.: Uninformative parameters and model selection using Akaike’s information criterion. J. Wildlife Manag. 74, 1175–1178 (2011)
Verhagen, S., Teunissen, P.J.G.: Least-squares estimation and Kalman filtering. In: Teunissen, P.J.G., Montenbruck, O. (eds.) Springer Handbook of Global Navigation Satellite Systems. SH, pp. 639–660. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-42928-1_22
Mudgal, A., Baffaut, C., Anderson, S.H., Sadler, E.J., Thompson, A.L.: APEX model assessment of variable landscapes on runoff and dissolved herbicides. Trans. ASABE 53, 1047–1058 (2010)
Weakley, A., Williams, J.A., Schmitteredgecombe, M., Cook, D.J.: Neuropsychological test selection for cognitive impairment classification: a machine learning approach. J. Clin. Exp. Neuropsychol. 37, 899–916 (2015)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (Nos. 61472282, 61672035, and 61872004), Key Laboratory of Metallurgical Emission Reduction & Resources Recycling in AHUT (KF 17-02), Anhui Province Funds for Excellent Youth Scholars in Colleges (gxyqZD2016068), Co-Innovation Center for Information Supply & Assurance Technology in AHU (ADXXBZ201705), and Anhui Scientific Research Foundation for Returness.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, G. et al. (2019). An Optimization Regression Model for Predicting Average Temperature of Core Dead Stock Column. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-26766-7_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26765-0
Online ISBN: 978-3-030-26766-7
eBook Packages: Computer ScienceComputer Science (R0)