References
- Ma W, Li H, Cui Y, et al. Optimization of desulphurization process using Lance injection in molten iron. ISIJ Int. 2017;57(2):214–219. doi: 10.2355/isijinternational.ISIJINT-2016-167
- Zhu C, Chen P, Li G, et al. A mathematical model of desulphurization kinetics for ultra-low-sulfur steels refining by powder injection during RH processing. ISIJ Int. 2016;56(8):1368–1377. doi: 10.2355/isijinternational.ISIJINT-2016-124
- Yue YJ, Yao YD, Zhao H, et al. BOF endpoint prediction based on multi-neural network model. Appl Mech Mater. 2014;441:666–669. doi: 10.4028/www.scientific.net/AMM.441.666
- Wang Z, Xie F, Wang B, et al. The control and prediction of end-point phosphorus content during BOF steelmaking process. Steel Res Int. 2014;85(4):599–606. doi: 10.1002/srin.201300194
- Patra S, Nayak J, Singhal LK, et al. Prediction of nitrogen content of steel melt during stainless steel making using AOD converter. Steel Res Int. 2017;88(5). https://onlinelibrary.wiley.com/doi/full/10.1002/srin.201600271
- Min H, Liu C. Endpoint prediction model for basic oxygen furnace steel-making based on membrane algorithm evolving extreme learning machine. Appl Soft Comput. 2014;19(1):430–437.
- Ahmad I, Kano M, Hasebe S. Prediction of molten steel temperature in steel making process with uncertainty by integrating gray-box model and bootstrap filter. J Chem Eng Jpn. 2014;47(11):827–834. doi: 10.1252/jcej.14we067
- Li W, Wangb X, Wanga X, et al. Endpoint prediction of BOF steelmaking based on BP neural network combined with improved PSO. Chem Eng. 2016;51:475–480. doi: 10.1016/j.cej.2016.05.083
- Pal S., Halder C. Optimization of phosphorous in steel Produced by basic oxygen steel making process using multi-objective evolutionary and genetic algorithms. Steel Res Int. 2017;88(3):1600193. doi: 10.1002/srin.201600193
- Chen X, She J, Chen X, et al. Discrete wavelet transfer based BPNN for calculating carbon efficiency of sintering process. J Adv Comput Intell Intell Inf. 2016;20(7):1070–1076. doi: 10.20965/jaciii.2016.p1070
- Wang H, Xu A, Ai L, et al. An integrated CBR model for predicting endpoint temperature of molten steel in AOD. ISIJ Int. 2012;52(1):80–86. doi: 10.2355/isijinternational.52.80
- Hu X, Wang Z, Wang G. Case-based reasoning(CBR) model for ultra-fast cooling in plate mill. Chin J Mech Eng. 2014;27(6):1264–1271. doi: 10.3901/CJME.2014.0819.136
- Feng K, He D, Xu A, et al. End temperature prediction of molten steel in LF based on CBR-BBN. Steel Res Int. 2016;87(1):79–86. doi: 10.1002/srin.201400512
- He Y, Li G, Sun Y, et al. Temperature intelligent prediction model of coke oven flue based on CBR and RBFNN. Int J Comput Sci Math. 2018;9(4):327–339. doi: 10.1504/IJCSM.2018.094654
- Liang Y, Wang H, Xu A, et al. A two-step case-based reasoning method based on attributes reduction for predicting the endpoint phosphorus content. ISIJ Int. 2015;55(5):1035–1043. doi: 10.2355/isijinternational.55.1035
- Ming T, Bi J, Ding J. Hybrid intelligent modelling and simulation for cold tandem rolling process. IET Control Theory Appl. 2016;10(12):1420–1430. doi: 10.1049/iet-cta.2015.0842
- He F, Xu A, Wang H, et al. End temperature prediction of molten steel in LF based on CBR. Steel Res Int. 2012;83(11):1079–1086. doi: 10.1002/srin.201200028