An application of physics-based and artificial neural networks-based hybrid temperature prediction schemes in a hot strip mill

Journal of Manufacturing Science and Engineering

Volumn 130, Issue 1, 2008, Pages 0145011-0145015

  Geerdes, Wouter M ^a   Alvarado, Miguel Ángel Torres ^b   Cabrera Ríos, Mauricio ^c   Cavazos, Alberto ^b  

^a UNIVERSITY OF TWENTE   (Netherlands)

^b UNIVERSIDAD AUTÓNOMA DE NUEVO LEÓN   (Mexico)

^c División de Estudios de Postgrado   (Mexico)

Author keywords

Artificial neural networks; Heat transfer models; Hot strip mill; Hybrid models; Temperature prediction

Indexed keywords

DIELECTRIC PROPERTIES; FORECASTING; HEAT TRANSFER; HOT ROLLING MILLS; STRIP METAL; STRIP MILLS;

DIMENSIONAL CONTROL; FINISHING MILLS; HEAT TRANSFER MODEL; HOT STRIP MILLS; HYBRID MODEL; PHYSICS-BASED MODELS; PREDICTION CAPABILITY; TEMPERATURE PREDICTION;

NEURAL NETWORKS;

EID: 46649105274     PISSN: 10871357     EISSN: None     Source Type: Journal    
DOI: 10.1115/1.2783223     Document Type: Article

References (23)

1. Martinetz, T., Protzel, P., Gramchow, O., and Sogel, G., 1994, "Neural Network Control for Rolling Mills," Elite Foundation 1997; EUFIT 94, pp. 147-152.
2. Sato, N., Kamada, N., Naito, S., Fukushima, T., and Fujino, M., 1992 "Application of fuzzy Control System to Hot Strip Mill," Proc. of International Conference on Industrial Electronics, Control & Instrumentation.
3. Watanabe, T., Narazaki, H., Kitamura, A., Takahashi, Y., and Hasegawa, H., 1997, "A New Mill-Setup System for Hot Strip Rolling Mill That Integrates a Process Model and Expertise," IEEE International Conference on Computational Cybernetics and Simulation.
4. Lee, D. Y., and Cho, H. S., 1999, "A Neural Network Approach to the Control of the Plate Width in Hot Plate Mills," IEEE International Joint Conference on Neural Networks.
5. Bissessur, Y., Martin, E. B., Morris, A. J., and Kitson, P., 2000, "Fault Detection in Hot Steel Rolling Using Neural Networks and Multivariate Statistics," IEEE Proceedings on Control Theory and Applications.
6. Kim, Y. S., Yum, B. J., and Kim, M., 2001, "Robust Design of Artificial Neural Network for Roll Force Prediction in Hot Strip Mill," IEEE International Joint Conference on Neural Network, Washington, DC, 4, pp. 2800-2804.
7. Chen, M. Y., and Linkens, D. A., 2001, "A Systematic Neuro Fuzzy Modeling Framework With and Application to Material Property Prediction," IEEE Trans. Syst., Man, Cybern., Part B: Cybern., 31(5), pp. 781-790.
8. Maheral, P., Ide, K., Gomi, T., Pussegoda, N., and Too, J. J. M., 1995, "Artificial Intelligence Technique in the Hot Rolling of Steel," IEEE Canadian Conference on Electrical and Computer Engineering, Montreal, pp. 507-510.
9. Schlang, M., Lang, B., Poppe, T., Runkler, T., Weinzierl, K., 2001, "Current and Future Development in Neural Computation in Steel Processing," Control Eng. Pract., 9(9), pp. 975-986.
10. Vermeulen, W., Brodin, A., and van der Zwaag, S., 1997, "Prediction of the Measured Temperature After the last Finishing Stand Using Artificial Neural Networks," Steel Res., 68(1), pp. 20-26.
11. Loney, D., Roberts, I., and Watson, J., 1997, "Modelling of the Hot Strip Mill Runout Table Using Artificial Neural Networks," Ironmaking Steelmaking, 24(1), pp. 34-39.
12. Méndez, G. M., Cavazos, A., Soto, R., and Leduc, L. A., 2006, "Entry Temperature Prediction of a Hot Strip Mill by a Hybrid Learning Type-2 FLS," J. Intell. Fuzzy Syst., 17(6), pp. 583-596.
13. Linkens, D. A., Beynon, J. H., and Sellars, C. M., 1997, "Grey Box Modelling Methodologies and Their Application to Materials Processing," Proc. of International Conference IPMM 97, Australasia-Pacific Forum on Intelligent Processing and Manufacturing of Materials, Gold Coast, Australia, July 14-17, pp. 676-682.
14. Thompson, M. L., and Kramer, M. A., 1994, "Modeling Chemical Processes Using Prior Knowledge and Neural Networks," AIChE J., 40, pp. 1328-1340.
15. Rooke, P. E., and Wang, H., 2002, "Applying Combined Neural Network and Physical Modelling to the Retention Process in Papermaking," Appita J., 55(4), pp. 281-286.
16. Hodgson, P. D., Kong, L. X., and Davis, C. H. J., 1999, "The Prediction of the Hot Strength in Steel With an Integrated Phenomenological and Artificial Neural Network Model," J. Mater. Process. Technol., 87(1), pp. 131-138.
17. Potocnik, P., 2003, "Neural Net Based Hybrid Modeling of Methanol Syntesis Process," Neural Processing Letters, 11, pp. 219-228.
18. Lauret, P., Boyer, H., and Gatina, J. C., 2001, "Hybrid Modelling of the Sucrose Crystal Growth Rate," Int. J. Model. Simulat., 21(1), pp. 23-29.
19. Anderson, S., McAvoy, T. J., and Hao, O. J., 2000, "Use of Hybrid Models in Wastewater Systems," Ind. Eng. Chem. Res., 39(6), pp. 1694-1704.
20. Zhu, Q., Abbod, M. F., Talamantes-Silva, J., Sellars, C. M., Linkens, D. A., and Beynon, J. H., 2003, "Hybrid Modelling of Aluminium-Magnesium Alloys During Thermomecanical Processing in Terms of Physically-based, Neuro-Fuzzy and Finite Element Models," Acta Mater., 51, pp. 5051-5062.
21. Kong, L. X., and Hodgson, P. D., 2003, "Prediction of Hot Strength of Steels With Advanced Models," Key Eng. Mater., 233-236, pp. 743-748.
22. Harding, R. A., 1976, "Temperature and Structural Changes During Hot Rolling," Ph.D. thesis.
23. Méndez, G. M., Leduc, L. A., Colás, R., Cavazos, A., and Soto, R., 2006, "Modelling the Recalescence After Bar Reduction During Hot Rolling," Iron-making Steelmaking, 33(6), pp. 484-492.