The set-up of the cooling water applied to the strip as it traverses the runout table in order to achieve the coiler entry temperature was made by an intelligent model implemented using interval type-2 fuzzy logic systems. The model uses as inputs the targets for coiling entry temperature, strip thickness, finish mill exit temperature and finishing mill exit speed. The experiments of this application were carried out for three different types of coil in a real hot strip mill. The results proved the feasibility of the system developed for coiler entry temperature prediction. Comparison with the online type-1 fuzzy logic based model shows that the proposed interval type-2 fuzzy logic system improves performance in coiler entry temperature prediction under the tested condition.
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References
1.
MendelJ. M.: ‘Uncertain rule-based fuzzy logic systems: introduction and new directions’; 2001, Upper Saddle River, NJ, Prentice-Hall.
2.
TaylorB. N.KuyattC. E.: ‘Guidelines for evaluating and expressing the uncertainty of NIST measurement results’, Technical note1297; 1994, Gaitherburg, MD, NIST.
3.
WangL. X.: ‘Fuzzy systems are universal approximators’, Proc. IEEE Conf. on ‘Fuzzy systems’, San Diego, CA, USA, March 1992, IEEE, 1163–1170.
4.
WangL. X.: ‘Fuzzy systems as nonlinear mapping, a course in fuzzy systems and control’, 118–127; 1997, Upper Saddle River, NJ, Prentice-Hall.
5.
WangL. X.: ‘Fuzzy systems as nonlinear dynamic system identifiers’, Proc. 31st IEEE Conf. on ‘Decision and control’, Tucson, AZ, USA, December 1992, IEEE, 897–902.
6.
WangL. X.MendelJ. M.: ‘Back-propagation fuzzy systems as nonlinear dynamic system identifiers’, Proc. IEEE Conf. on ‘Fuzzy systems’, San Diego, CA, USA, March 1992, IEEE, 1409–1418.
7.
MartinetzT.ProtzelP.GramchowO.SorgelG.: ‘Neural network control for rolling mills’, Proc. Conf. EUFIT ’94, Achen, Germany, September 1994, ELITE Foundation, 147–152.
8.
ElectricGeneral: ‘Models users’ reference’, Manual1; 1993, Roanoke, VA, General Electric.
9.
SatoN.KamadaN.NaitoS.FukushimaT.FujinoM.: ‘Application of fuzzy control system to hot strip mill’, Proc. IEEE Int. Conf. on ‘Industrial electronics, control, instrumentation, and automation’, San Diego, CA, USA, November 1992, IEEE, 1202–1206.
10.
LeeD. Y.ChoH. S.: ‘A neural network approach to the control of the plate width in hot plate mills’, Proc. IEEE Int. Joint Conf. on ‘Neural networks’, Washington, DC, USA, July 1999, IEEE, Vol. 5, 3391–3396.
11.
BissessurY.MartinE. B.MorrisA. J.KitsonP.: ‘Fault detection in hot steel rolling using neural networks and multivariate statistics’, IEE Proc., Control Theory Appl., 2000, 147, 633–640.
YaoX.TieuA. K.FangX. D.FrancesD.: ‘Neural network application to head and tail width control in hot strip mill’, Proc. IEEE Int. Conf. on ‘Neural networks’, Perth, Australia, November–December1995, IEEE, Vol. 1, 433–437.
14.
KimY. S.YumB. J.KimM.: ‘Robust design of artificial neural network for roll force prediction in hot strip mill’, Proc. IEEE Int. Joint Conf. on ‘Neural network’, Washington, DC, USA, July 2001, IEEE, Vol. 4, 2800–2804.
15.
WatanabeT.NarazakiH.KitamuraA.TakahashiY.HasegawaH.: ‘A new mill-setup system for hot strip rolling mill that integrates a process model and expertise’, Proc. IEEE Int. Conf. on ‘Computational cybernetics and simulation’, Orlando, FL, USA, October 1997, Vol. 3, IEEE, 2818–2822.
16.
CastilloO.CazaresN.RicoD.: ‘Intelligent control of dynamic systems using type2 fuzzy logic and stability issues’, Int. Math. Forum, 2006, 1, 1371–1382.
17.
LiangQ.MendelJ. M.: ‘Interval type-2 fuzzy logic systems: theory and design’, Trans. Fuzzy Syst., 2000, 8, 535–550.
18.
MéndezM.CavazosA.LeducL.SotoR.: ‘Hot strip mill temperature prediction using hybrid learning interval singleton type-2 FLS’, Proc. IASTED Int. Conference on ‘Modeling and simulation’, Palm Springs, CA, USA, February 2003, IASTED, 380–385.
19.
MéndezM.CavazosA.LeducL.SotoR.: ‘Modeling of a hot strip mill temperature using hybrid learning for interval type-1 and type-2 non-singleton type-2 FLS’, IASTED Int. Conf. on ‘Artificial intelligence and applications’, Benalmádena, Spain, February2003, IASTED, 529–533.
20.
Predicted (blue) and measured (green) CLR entry temperatures for type-A coils using T1 FLS Méndez, et al. Modelling and control of coiling entry temperature using IT2 FLS 134 Ironmaking
