Haapala,
K. R.
,
Zhao,
F.
,
Camelio,
J.
,
Sutherland,
J. W.
,
Skerlos,
S. J.
,
Dornfeld,
D. A.
,
Jawahir,
I. S.
,
Clarens,
A. F.
, and
Rickli,
J. L.
, 2013, “
A Review of Engineering Research in Sustainable Manufacturing,” ASME J. Manuf. Sci. Eng.,
135(4), p. 041013.
[CrossRef]
Haapala,
K. R.
,
Catalina,
A. V.
,
Johnson,
M. L.
, and
Sutherland,
J. W.
, 2012, “
Development and Application of Models for Steelmaking and Casting Environmental Performance,” ASME J. Manuf. Sci. Eng.,
134(5), p. 051013.
[CrossRef]World Steel Association, 2015, “Steel Statistical Yearbook 2015.”
Wang,
J. K.
, and
Qiao,
F.
, 2014, “
Cost and Energy Consumption Collaborative Optimization for Sintering Burdening in Iron and Steel Enterprise,” 2014 IEEE International Conference on Automation Science and Engineering (CASE), Taipei, Taiwan, Aug. 18–22, pp. 486–491.
Lu,
Z. W.
, and
Cai,
J. J.
, 2010, Basis of Systemic Energy Conservation,
Science Press,
Beijing, China.
Castro,
J. A.
,
de Sazaki,
Y.
, and
Yagi,
J.
, 2012, “
Three Dimensional Mathematical Model of the Iron Ore Sintering Process Based on Multiphase Theory,” Mater. Res.,
15(6), pp. 848–858.
[CrossRef]
Muller,
J.
,
de Vries,
T. L.
,
Dippenaar,
B. A.
, and
Vreugdenburg,
J. C.
, 2015, “
A Finite Difference Model of the Iron Ore Sinter Process,” J. So. African Inst. Mining Metall.,
115(5), pp. 409–417.
Zhou,
H.
,
Zhao,
J. P.
,
Loo,
C. E.
,
Ellis,
B. G.
, and
Cen,
K. F.
, 2012, “
Numerical Modeling of the Iron Ore Sintering Process,” ISIJ Int.,
52(9), pp. 1550–1558.
[CrossRef]
Zhou,
H.
,
Zhao,
J. P.
,
Loo,
C. E.
,
Ellis,
B. G.
, and
Cen,
K. F.
, 2012, “
Model Predictions of Important Bed and Gas Properties During Iron Ore Sintering,” ISIJ Int.,
52(12), pp. 2168–2176.
[CrossRef]
Park,
H.-S.
, and
Dang,
X.-P.
, 2015, “
Multiobjective Optimization of the Heating Process for Forging Automotive Crankshaft,” ASME J. Manuf. Sci. Eng.,
137(3), p. 031011.
[CrossRef]
Nelson,
A. W.
,
Malik,
A. S.
,
Wendel,
J. C.
, and
Zipf,
M. E.
, 2014, “
Probabilistic Force Prediction in Cold Sheet Rolling by Bayesian Inference,” ASME J. Manuf. Sci. Eng.,
136(4), p. 041006.
[CrossRef]
Han,
Q. H.
,
Jin,
Y. L.
, and
Zhang,
J. H.
, 2005, “
Application of Neural Networks in the Prediction of Solid Fuel Consumption in Sintering Process,” Energy Metall. Ind.,
24(2), pp. 9–11.
He,
G. Q.
,
Sun,
Y.
, and
Wang,
B.
, 2010, “
An Improved Neural Network Algorithm and Its Application in Sinter Cost Prediction,” Comput. Eng. Sci.,
32(8), pp. 138–140.
Meng,
H.
,
Qiao,
F.
, and
Li,
L.
, 2012, “
Predictive Model of Energy Consumption in Sintering Process Based on BP Neural Network,” Mech. Eng.,
2, pp. 45–47.
Feng,
Z.
,
Zhang,
H.
, and
Wang,
Y.
, 2012, “
Study on Prediction and Optimization of Sintering Process Energy Consumption,” Sinter Pelletizing,
37(6), pp. 13–17.
Attanasio,
A.
,
Ceretti,
E.
,
Giardini,
C.
, and
Cappellini,
C.
, 2013, “
Tool Wear in Cutting Operations: Experimental Analysis and Analytical Models,” ASME J. Manuf. Sci. Eng.,
135(5), p. 051012.
[CrossRef]
Geerdes,
W. M.
,
Alvarado,
M. A. T.
,
Cabrera-Ríos,
M.
, and
Cavazos,
A.
, 2008, “
An Application of Physics-Based and Artificial Neural Networks-Based Hybrid Temperature Prediction Schemes in a Hot Strip Mill,” ASME J. Manuf. Sci. Eng.,
130(1), p. 014501.
[CrossRef]
Omitaomu,
O. A.
,
Jeong,
M. K.
,
Badiru,
A. B.
, and
Hines,
J. W.
, 2006, “
On-Line Prediction of Motor Shaft Misalignment Using Fast Fourier Transform Generated Spectra Data and Support Vector Regression,” ASME J. Manuf. Sci. Eng.,
128(4), p. 1019.
[CrossRef]
Asilturk,
İ.
,
Kahramanli,
H.
, and
El Mounayri,
H.
, 2012, “
Prediction of Cutting Forces and Surface Roughness Using Artificial Neural Network (ANN) and Support Vector Regression (SVR) in Turning 4140 Steel,” Mater. Sci. Technol.,
28(8), pp. 980–986.
[CrossRef]
Vapnik,
V.
, 1995, The Nature of Statistical Learning Theory,
Springer Verlag,
New York.
Huang,
G. B.
,
Zhu,
Q.-Y.
, and
Siew,
C.-K.
, 2004, “
Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks,” 2004 IEEE International Joint Conference on Neural Networks, July 25–29, pp. 985–990.
Han,
M.
, and
Liu,
C.
, 2013, “
Endpoint Prediction Model for Basic Oxygen Furnace Steel-Making Based on Membrane Algorithm Evolving Extreme Learning Machine,” Appl. Soft Comput. J.,
19, pp. 430–437.
[CrossRef]
Orair,
G. H.
,
Teixeira,
C. H. C.
,
Meira,
W.
,
Wang,
Y.
, and
Parthasarathy,
S.
, 2010, “
Distance-Based Outlier Detection,” Proc. VLDB Endow.,
3(1–2), pp. 1469–1480.
[CrossRef]
Breunig,
M. M.
,
Kriegel,
H.-P.
,
Ng,
R. T.
, and
Sander,
J.
, 2000, “
LOF: Identifying Density-Based Local Outliers,” 2000 ACM SIGMOD International Conference on Management of Data, ACM, Dallas, TX, pp. 93–104.
Song,
Q.
, and
Wang,
A. M.
, 2009, “
Simulation and Prediction of Alkalinity in Sintering Process Based on Grey Least Squares Support Vector Machine,” J. Iron Steel Res. Int.,
16(5), pp. 1–6.
[CrossRef]
Fu,
J.
,
Huang,
C. Q.
,
Xing,
J. G.
, and
Zheng,
J. B.
, 2012, “
Pattern Classification Using an Olfactory Model With PCA Feature Selection in Electronic Noses: Study and Application,” Sensors,
12(3), pp. 2818–2830.
[CrossRef] [PubMed]
Rana,
M.
,
Koprinska,
I.
, and
Agelidis,
V. G.
, 2012, “
Feature Selection for Electricity Load Prediction Mashud,” 19th International Conference on Neural Information Processing, ICONIP 2012, Doha, Qatar, T. Huang, Z. Zeng, C. Li, and C. S. Leung, eds., Springer, Berlin, pp. 526–534.
Islam,
T.
,
Srivastava,
P. K.
,
Dai,
Q.
,
Gupta,
M.
, and
Zhuo,
L.
, 2015, “
Rain Rate Retrieval Algorithm for Conical-Scanning Microwave Imagers Aided by Random Forest, RReliefF, and Multivariate Adaptive Regression Splines (RAMARS),” IEEE Sens. J.,
15(4), pp. 2186–2193.
[CrossRef]
Robnik-Šikonja,
M.
, and
Kononenko,
I.
, 2003, “
Theoretical and Empirical Analysis of ReliefF and RReliefF,” Mach. Learn.,
53(1–2), pp. 23–69.
[CrossRef]
Prakasvudhisarn,
C.
,
Trafalis,
T. B.
, and
Raman,
S.
, 2003, “
Support Vector Regression for Determination of Minimum Zone,” ASME J. Manuf. Sci. Eng.,
125(4), pp. 736–739.
[CrossRef]
Huang,
G.-B.
,
Wang,
D. H.
, and
Lan,
Y.
, 2011, “
Extreme Learning Machines: A Survey,” Int. J. Mach. Learn. Cybern.,
2(2), pp. 107–122.
[CrossRef]
Tian,
H. X.
, and
Mao,
Z. Z.
, 2010, “
An Ensemble ELM Based on Modified AdaBoost.RT Algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace,” IEEE Trans. Autom. Sci. Eng.,
7(1), pp. 73–80.
[CrossRef]
Lv,
W.
,
Mao,
Z. Z.
,
Yuan,
P.
, and
Jia,
M. X.
, 2014, “
Pruned Bagging Aggregated Hybrid Prediction Models for Forecasting the Steel Temperature in Ladle Furnace,” Steel Res. Int.,
85(3), pp. 405–414.
[CrossRef]
Tian,
X. H.
, and
Mao,
Z. Z.
, 2009, “
Multi-Model Prediction of Molten Steel Temperature Based on Bagging,” Control Decis.,
24(5), pp. 687–691.
Li,
H.
, and
Wang,
B.
, 2014, “
Analysis of Environmental and Economic Benefits in Iron and Steel Enterprises by Entropy Weight Fuzzy Comprehensive Evaluation Model,” Environ. Eng. Manage. J.,
13(5), pp. 1213–1219.
Liu,
X. Y.
,
Gao,
C. H.
, and
Li,
P.
, 2012, “
A Comparative Analysis of Support Vector Machines and Extreme Learning Machines,” Neural Networks,
33, pp. 58–66.
[CrossRef] [PubMed]
Drucker,
H.
,
Burges,
C. J. C.
,
Kaufman,
L.
,
Smola,
A.
, and
Vapnik,
V.
, 1997, “
Support Vector Regression Machines,” Advances in Neural Information Processing Systems 9,
M. I. Jordan
, ed.,
MIT Press,
Cambridge, MA, pp. 155–161.
Wang,
J. K.
,
Qiao,
F.
,
Zhu,
J.
, and
Ni,
J. C.
, 2014, “
SVR-Based Predictive Models of Energy Consumption and Performance Criteria for Sintering,” J. Tongji Univ. Nat. Sci.,
42(8), pp. 52–56.
Li,
Y.
,
Wu,
M.
,
Cao,
W. H.
,
Lai,
X. Z.
, and
Wang,
C. S.
, 2011, “
Intelligent Integrated Model With Cascade Structure for Sinter Quality Prediction,” Chin. J. Sci. Instrum.,
32(8), pp. 1742–1750.