Table of Contents Author Guidelines Submit a Manuscript
Discrete Dynamics in Nature and Society
Volume 2015, Article ID 483674, 13 pages
http://dx.doi.org/10.1155/2015/483674
Research Article

Optimal Control Problem of Converter Steelmaking Production Process Based on Operation Optimization Method

State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China

Received 3 September 2014; Revised 13 January 2015; Accepted 14 January 2015

Academic Editor: Peng Shi

Copyright © 2015 Jun Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. E. Marlin and A. N. Hrymak, “Real-time operations optimization of continuous processes,” AIChE Symposium Series, vol. 316, pp. 156–164, 1997. View at Google Scholar
  2. W. S. Yip and T. E. Marlin, “Multiple data sets for model updating in real-time operations optimization,” Computers and Chemical Engineering, vol. 26, no. 10, pp. 1345–1362, 2002. View at Publisher · View at Google Scholar · View at Scopus
  3. N. Peters, M. Guay, and D. DeHaan, “Real-time dynamic optimization of batch systems,” Journal of Process Control, vol. 17, no. 3, pp. 261–271, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. V. Adetola and M. Guay, “Integration of real-time optimization and model predictive control,” Journal of Process Control, vol. 20, no. 2, pp. 125–133, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. L. A. Alvarez and D. Odloak, “Robust integration of real time optimization with linear model predictive control,” Computers and Chemical Engineering, vol. 34, no. 12, pp. 1937–1944, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. G. de Souza, D. Odloak, and A. C. Zanin, “Real time optimization (RTO) with model predictive control (MPC),” Computers and Chemical Engineering, vol. 34, no. 12, pp. 1999–2006, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Ochoa, J.-U. Repke, and G. Wozny, “Integrating real-time optimization and control for optimal operation: application to the bio-ethanol process,” Biochemical Engineering Journal, vol. 53, no. 1, pp. 18–25, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. M. C. Wellons, A. V. Sapre, A. I. Chang, and T. L. Laird, “On-line power plant optimization improves Texas refiner's bottom line,” Oil and Gas Journal, vol. 92, no. 20, pp. 53–58, 1994. View at Google Scholar · View at Scopus
  9. A. M. Eliceche, N. C. Petracci, P. Hoch, and E. A. Brignole, “Optimal operation of an ethylene plant at variable feed conditions,” Computers and Chemical Engineering, vol. 19, no. 1, pp. 223–228, 1995. View at Publisher · View at Google Scholar · View at Scopus
  10. N. Petracci, A. M. Eliceche, A. Bandoni, and E. A. Brignole, “Optimal operation of an ethylene plant utility system,” Computers and Chemical Engineering, vol. 17, pp. 147–152, 1993. View at Publisher · View at Google Scholar · View at Scopus
  11. Z. J. Shao, J. L. Wang, and J. X. Qian, “Real-time optimization of acetaldehyde production process,” Developments in Chemical Engineering and Mineral Processing, vol. 13, no. 3-4, pp. 249–258, 2005. View at Google Scholar · View at Scopus
  12. J. Z. Lu, “Challenging control problems and emerging technologies in enterprise optimization,” in Proceedings of the 6th IFAC Symposium on Dynamic and Control of Process Systems, pp. 23–34, Jejudo Island, Republic of Korea, June 2001.
  13. D. Bonvin and B. Srinivasan, “On the role of the necessary conditions of optimality in structuring dynamic real-time optimization schemes,” Computers and Chemical Engineering, vol. 51, pp. 172–180, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. L. T. Biegler, “Technology advances for dynamic real-time optimization,” Computer Aided Chemical Engineering, vol. 27, pp. 1–6, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Tang, G. Wang, and Z.-L. Chen, “Integrated charge batching and casting width selection at Baosteel,” Operations Research, vol. 62, no. 4, pp. 772–787, 2014. View at Publisher · View at Google Scholar
  16. L. Tang, J. Liu, A. Rong, and Z. Yang, “Mathematical programming model for scheduling steelmaking-continuous casting production,” European Journal of Operational Research, vol. 120, no. 2, pp. 423–435, 2000. View at Publisher · View at Google Scholar · View at Scopus
  17. L. Tang, P. B. Luh, J. Liu, and L. Fang, “Steel-making process scheduling using Lagrangian relaxation,” International Journal of Production Research, vol. 40, no. 1, pp. 55–70, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Wu, C. N. Yang, M. X. You, X. K. Xing, and Y. Hu, “A temperature prediction model of converters based on gas analysis,” Procedia Earth and Planetary Science, vol. 2, pp. 14–19, 2011. View at Publisher · View at Google Scholar
  19. H.-Y. Wen, Q. Zhao, Y.-R. Chen, M.-C. Zhou, M. Zhang, and L.-F. Xu, “Converter end-point prediction model using spectrum image analysis and improved neural network algorithm,” Optica Applicata, vol. 38, no. 4, pp. 693–704, 2008. View at Google Scholar · View at Scopus
  20. L.-F. Xu, W. Li, M. Zhang, S.-X. Xu, and J. Li, “A model of basic oxygen furnace (BOF) end-point prediction based on spectrum information of the furnace flame with support vector machine (SVM),” Optik, vol. 122, no. 7, pp. 594–598, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Han, Y. Li, and Z. Cao, “Hybrid intelligent control of BOF oxygen volume and coolant addition,” Neurocomputing, vol. 123, pp. 415–423, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. L. Yu, “An evolutionary programming based asymmetric weighted least squares support vector machine ensemble learning methodology for software repository mining,” Information Sciences, vol. 191, no. 15, pp. 31–46, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Publisher · View at Google Scholar · View at MathSciNet
  24. L. X. Tang, Y. Dong, and J. Y. Liu, “Differential evolution with an individual-dependent mechanism,” IEEE Transactions on Evolutionary Computation, 2014. View at Publisher · View at Google Scholar
  25. L. X. Tang, Y. Zhao, and J. Y. Liu, “An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 2, pp. 209–225, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. K. E. Parsopoulos, D. K. Tasoulis, and M. N. Vrahatis, “Multiobjective optimization using parallel vector evaluated particle swarm optimization,” in Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, pp. 823–828, Innsbruck, Austria, February 2004. View at Scopus
  27. J. Liu, X. Ren, and H. Ma, “A new PSO algorithm with random C/D switchings,” Applied Mathematics and Computation, vol. 218, no. 19, pp. 9579–9593, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. J. Brest, S. Greiner, B. Bošković, M. Mernik, and V. Zumer, “Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 6, pp. 646–657, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. A. K. Qin, V. L. Huang, and P. N. Suganthan, “Differential evolution algorithm with strategy adaptation for global numerical optimization,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 398–417, 2009. View at Publisher · View at Google Scholar · View at Scopus