Sulphide capacity prediction of CaO–SiO₂–MgO–Al₂O₃ slag system by using regularized extreme learning machine

References

• Ma A, Mostaghel S, Chattopadhyay K. Development of an artificial neural network to predict sulphide capacities of CaO-SiO2-Al2O3-MgO slag system. ISIJ Int. 2017;57(1):114–122. doi: 10.2355/isijinternational.ISIJINT-2016-368
   Web of Science ®Google Scholar
• Berger A, Pezzin R, Grillo F, et al. Analysis of the influence of adding CaF2 and Na2O to steel desulfurizing mixtures using computational thermodynamics. J Mater Res Technol. 2019;8(5):4651–4658. doi: 10.1016/j.jmrt.2019.08.009
   Google Scholar
• Matsubae K, Kubo H, Nagasaka T. Recycling effects of residual slag after magnetic separation for phosphorus recovery from hot metal dephosphorization slag. ISIJ Int. 2010;50(1):65–70. doi: 10.2355/isijinternational.50.65
   Web of Science ®Google Scholar
• Duan C, Shi X, Wang F, et al. Investigation of desulfurization of inconel 718 superalloys by ESR type slags with different TiO2 content. J Mater Res Technol. 2019;8(3):2508–2516. doi: 10.1016/j.jmrt.2019.01.027
   Google Scholar
• Fincham C, Richardson F. The behaviour of sulphur in silicate and aluminate melts. P Roy Soc A. 1954;223(1152):40–62.
   Google Scholar
• Duffy J, Ingram M. Optical basicity-IV: influence of electro negativity on the Lewis basicity and solvent properties of molten oxyanion salts and glasses. J Inorg Nucl Chem. 1975;37(5):1203–1206. doi: 10.1016/0022-1902(75)80469-6
   Web of Science ®Google Scholar
• Sosinky D, Sommerville I. The composition and temperature dependence of the sulfide capacity of metallurgical slags. Metall Mater Trans B. 1986;17(2):331–337. doi: 10.1007/BF02655080
   Google Scholar
• Young R, Duffy J, Hassall G, et al. Use of optical basicity concept for determining phosphorus and sulphur slag-metal partition. Ironmak Steelmak. 1992;19(3):201–219.
   Web of Science ®Google Scholar
• Zhang G, Chou K, Pal U. Estimation of sulfide capacities of multicomponent slags using optical basicity. ISIJ Int. 2013;53(5):761–767. doi: 10.2355/isijinternational.53.761
   Web of Science ®Google Scholar
• Bale C, Chartrand P, Degterov S, et al. Factsage thermo-chemical software and databases. Comput Coup Pha Diag Therm. 2002;26(2):189–228.
   Google Scholar
• Nzotta M, Du S, Seetharaman S. Sulphide capacities in some multi component slag system. ISIJ Int. 1998;38(11):1170–1179. doi: 10.2355/isijinternational.38.1170
   Web of Science ®Google Scholar
• Pezzin R, Berger A, Grillo F, et al. Analysis of the influence of the solid and liquid phases on steel desulfurization with slags from the CaO-Al2O3 systems using computational thermodynamics. J Mater Res Technol. 2020;9(1):838–846. doi: 10.1016/j.jmrt.2019.11.023
   Google Scholar
• Derin B, Suzuki M, Tanaka T. Sulphide capacity prediction of molten slags by using a neural network approach. ISIJ Int. 2010;50(8):1059–1063. doi: 10.2355/isijinternational.50.1059
   Web of Science ®Google Scholar
• Huang G, Huang G, Song S, et al. Trends in extreme learning machines: a review. Neural Netw. 2015;61:32–48. doi: 10.1016/j.neunet.2014.10.001
   PubMed Web of Science ®Google Scholar
• Huang G, Zhu Q, Siew C. Extreme learning machine: theory and applications. Neurocomputing. 2006;70:489–501. doi: 10.1016/j.neucom.2005.12.126
   Web of Science ®Google Scholar
• Chen H, Yang J, Lu X, et al. Quality prediction of the continuous casting bloom based on the extreme learning machine. Chin J Eng-en. 2018;40(7):815–821.
   Google Scholar
• Guan D, Tang X, Wang L, et al. Extreme learning machine with superpixel-guided composite kernels for SAR image classification. IEICE T Inf Syst. 2018;E101-D(6):1703–1706. doi: 10.1587/transinf.2017EDL8281
   Web of Science ®Google Scholar
• Zou L, Zhang J, Liu Q, et al. Prediction of central carbon segregation in continuous casting billet using a regularized extreme learning machine model. Metals. 2019;9(12):1312. doi: 10.3390/met9121312
   Web of Science ®Google Scholar
• Carl A, Du S. Sulfide capacity in ladle slag at steelmaking temperatures. Metall Mater Trans B. 2015;46B:2609–2615.
   Google Scholar
• Drakaliysky E, Du S, Seetharaman S. An experimental study of the sulphide capacities in the system Al2O3-CaO-SiO2. Can Metall Quart. 1997;36(2):115–120.
   Web of Science ®Google Scholar
• Ohta M, Kubo T, Morita K. Effects of CaF2, MgO and SiO2 addition on sulfide capacities of the CaO-Al2O3 slag. Tetsu-to-Hagane. 2003;89(7):742–749. doi: 10.2355/tetsutohagane1955.89.7_742
   Web of Science ®Google Scholar
• Hayakawa H, Hasegawa M, Oh-nuki K, et al. Sulphide capacities of CaO-SiO2-Al2O3-MgO slags. Steel Res Int. 2006;77(1):14–20. doi: 10.1002/srin.200606124
   Web of Science ®Google Scholar
• Taniguchi Y, Sano N, Seetharaman S. Sulphide capacities of CaO-Al2O3-SiO2-MgO-MnO slags in the temperature range 1673–1773K. ISIJ Int. 2009;49(2):156–163. doi: 10.2355/isijinternational.49.156
   Web of Science ®Google Scholar
• Hino M, Kitagawa S, Banya S. Sulphide capacities of CaO-Al2O3-MgO and CaO-Al2O3-SiO2 slags. ISIJ Int. 1993;33(1):36–42. doi: 10.2355/isijinternational.33.36
   Web of Science ®Google Scholar
• Ma X, Chen M, Xu H, et al. Sulphide capacity of CaO-SiO2-Al2O3-MgO system relevant to low MgO blast furnace slags. ISIJ Int. 2016;56(12):2126–2131. doi: 10.2355/isijinternational.ISIJINT-2016-274
   Web of Science ®Google Scholar
• Seo J, Kim S. The sulphide capacity of CaO-SiO2-Al2O3-MgO(-FeO) smelting reduction slags. Steel Res Int. 1999;70(6):203–208. doi: 10.1002/srin.199905627
   Web of Science ®Google Scholar
• Adolfo F, Carl A, Du S. Experimental determination of sulphide capacities of blast furnace slags with higher MgO contents. Ironmak Steelmak. 2019;46(3):207–210. doi: 10.1080/03019233.2017.1366089
   Web of Science ®Google Scholar
• Adolfo F, Shu Q, Du S. Sulfide capacities in the Al2O3-CaO-MgO-SiO2 system. Steel Res Int. 2018;89:1–7.
   Web of Science ®Google Scholar
• Shankar A, Gornerup M, Lahiri A, et al. Sulfide capacity of high alumina blast furnace slags. Metall Mater Trans B. 2006;37B:941–947. doi: 10.1007/BF02735016
   Web of Science ®Google Scholar
• Sheng Z, Xie S, Pan C. Probability theory and mathematical statistics. Beijing: Higher Education Press; 2008.
   Google Scholar
• Huang G, Zhu Q, Siew C. Extreme learning machine: a new learning scheme of feed forward neural networks. In Proceedings of the international joint conference on neural networks (IJCNN 2004); 2004 July 25–29; Budapest, Hungary. p. 985–990.
   Google Scholar
• Deng W, Zheng Q, Chen L, et al. Research on extreme learning of neural networks. Chin J Comput. 2010;33(2):279–287. doi: 10.3724/SP.J.1016.2010.00279
   Google Scholar
• Tian H, Mao Z, Wang Y. Hybrid modeling of molten steel temperature prediction in LF. ISIJ Int. 2008;48(1):58–62. doi: 10.2355/isijinternational.48.58
   Web of Science ®Google Scholar