THERMAL SCIENCE

International Scientific Journal

SHORT-TERM AND LONG-TERM THERMAL PREDICTION OF A WALKING BEAM FURNACE USING NEURO-FUZZY TECHNIQUES

ABSTRACT
The walking beam furnace (WBF) is one of the most prominent process plants often met in an alloy steel production factory and characterized by high non-linearity, strong coupling, time delay, large time-constant and time variation in its parameter set and structure. From another viewpoint, the WBF is a distributed-parameter process in which the distribution of temperature is not uniform. Hence, this process plant has complicated non-linear dynamic equations that have not worked out yet. In this paper, we propose one-step non-linear predictive model for a real WBF using non-linear black-box sub-system identification based on locally linear neuro-fuzzy (LLNF) model. Furthermore, a multi-step predictive model with a precise long prediction horizon (i.e., ninety seconds ahead), developed with application of the sequential one-step predictive models, is also presented for the first time. The locally linear model tree (LOLIMOT) which is a progressive tree-based algorithm trains these models. Comparing the performance of the one-step LLNF predictive models with their associated models obtained through least squares error (LSE) solution proves that all operating zones of the WBF are of non-linear sub-systems. The recorded data from Iran Alloy Steel factory is utilized for identification and evaluation of the proposed neuro-fuzzy predictive models of the WBF process.
KEYWORDS
PAPER SUBMITTED: 2012-04-10
PAPER REVISED: 2012-05-10
PAPER ACCEPTED: 2012-10-14
DOI REFERENCE: https://doi.org/10.2298/TSCI120410210B
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2015, VOLUME 19, ISSUE 2, PAGES [703 - 721]
REFERENCES
  1. Shaoyuan, Li., Chen, Q., Huang, G., Dynamic temperature modeling of continuous annealing furnace using GGAP-RBF neural network, neurocomputing, vol.69, (2006), pp.524-536
  2. Zhang, B., Chen, Z., Xu, L., Wang, J., Zhang, J., Shao, H., The Modeling and Control of A Reheating Furnace, Proceedings of the American Control Conference, (2002), pp.3823-3828
  3. Laurinen, P., Roning, J., An adaptive neural network model for predicting the post roughing mill temperature of steel slabs in the reheating furnace, Materials Processing Technology, (2005), pp.423-430
  4. Chen, Q., Li, S., Xi, Y., Huang, G., Furnace Temperature Modeling for Continuous Annealing Process Based on Generalized Growing and Pruning RBF Neural Network, Advances in Neural Networks, vol.3174, (2004), pp.755-760
  5. Banadaki, H. D., Nozari, H. A., Kakahaji, H., Non-linear Simulator model Identification of a Walking Beam Furnace Using Recurrent Local Linear Neuro-Fuzzy Network, International Journal of Control and Automation, vol.4, no.4 , (2011), pp.123-134
  6. Kusters, A.,van Ditzhuijzen, G.A.J.M., MIMO system identification of a slab reheating furnace, Proceedings of the Third IEEE Conference on Control Applications, (1994), pp.3097-1563
  7. Gobbak, K.A., Raghavendran, H., Internal Feedback Neuron Networks for Modeling of an Industrial Furnace, Neural Networks, International Conference, (1997),pp.1948-1953
  8. Liao, Y., Wu, M., She, J., Modeling of reheating-furnace dynamics using neural network based on improved sequential-learning algorithm, Computer Aided Control System Design, (2006), pp.3175-318
  9. Xuegang, S., Chao, Y., Yihui, C., Dynamic Modeling of Reheat-Furnace Using Neural Network based on PSO Algorithm, International Conference on Mechatronics and Automation, (2009), pp.3097-3101
  10. Pongam, T., Srisertpol, J., Khomphis, V., Open-loop Identification of the Mathematical Model of the Reheating Furnace Walking Hearth Type in Manufacturing Process, International Conference on System Modeling and Optimization, (2012), vol.23, pp.24-30
  11. Ogawa, M., Yichun, Y., Kawanari, S., Ogai, H., Long-term prediction of industrial furnace by Extended Sequential Prediction method of LOM, SICE Annual Conference, (2010), pp.1490-1493
  12. Leea, D., Leeb, Y., Application of neural-network for improving accuracy of roll-force model in hot-rolling mill, Control Engineering Practice, (2002), vol.10, pp.473-478
  13. Kaiju, Z., Di, J., Cheng, S., Fuzzy neural network's application in furnace temperature compensation based on rolling information, IFAC World Congress, (2005),vol.16, part 1
  14. Schlanga, M., Langb, B., Poppeb, T., Runklerb, T., Weinzierlc, K., Current and future development in neural computation in steel processing, Control Engineering Practice, (2001), vol.9, pp.975-986
  15. Roger Jang, J., Sun, C., Mizutani, E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hal Inc.l, 1997
  16. Razavi-Far, R., Davilu, H., Palade, V., Lucas, C., Model-based fault detection and isolation of a steam generator using neuro-fuzzy networks, neurocomputing, vol.72, (2009), pp.2939-2951
  17. Sadeghian, M., Fatehi, A., Identification of Non-linear Predictor and Predictor Models of a Cement Rotary Kiln by Locally Linear Neuro-Fuzzy Technique, World Academy of Science, Engineering and Technology, (2009), pp.1121-1127
  18. Nozari, H. A., Banadaki, H. D., Mokhtare, M., Vahed, S. H., Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks, Journal of Zhejiang University - Science C, vol.13, no.6, (2012), pp.403-412
  19. Mohammadzaheri, M., Lei, C., Intelligent Modeling of MIMO Non-linear Dynamic Process Plants for Predictive Control Purposes, Proceedings of the 17th World Congress the International Federation of Automatic Control Seoul, (2008), pp.12401-12406
  20. Nelles, O., Local linear model tree for on-line identification of time variant non-linear dynamic systems, International Conference on Artificial Neural Networks, vol.1112,(1996), pp.115-120
  21. Nelles, O., Non-linear system identification, Springer Inc., 2001
  22. Ljung, L., System Identification Theory for the user, Prentice Hall Inc
  23. Nozari, H. A., Shoorehdeli, M. A., Simani, S., Banadaki, H. D, Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques, neurocomputing, vol.91, (2012), pp.29-47
  24. Nelles, O., Isermann, R., Basis function networks for interpolation of locally linear models, Proc. of IEEE Conference on Decision and Control, (1996), pp.470-475
  25. Judd, K., Small, M.,Towards long-term prediction, Journal of Physica, vol.136, (2000), pp.31-44

© 2020 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence