Advertisement

Thermal Engineering Processes Simulation Based on Artificial Intelligence

  • Xiaoqi Peng
  • Yanpo Song
Chapter
  • 777 Downloads

Abstract

Because of the complexity of nonferrous metallurgical processes, it is difficult to build accurate mechanistic models for them. While, artificial intelligence(AI) modeling method avoids the complex mechanism analysis and describes the object process by its historic data, therefore, it is very advantageous especially for complex industrial process in which historic process data have been accumulated plentifully. In this chapter, several important AI methods and their applications are introduced, based on these, two AI modeling methods for multi-variable systems are proposed: one is fuzzy adaptive modeling method, which has been applied to develop the fuzzy adaptive optimal decision model of the submerged arc furnace; another is fuzzy neural network adaptive modeling method, which has been applied to develop fuzzy neural network adaptive optimal decision model of the electric furnace for cleaning slag. Both of the models are self-learning and self-adaptive, and are able to avoid the disadvantage of the static decision-making model based on the calculation of material balance and thermal balance in a smelting process. They have achieved good performance in practice.

Keywords

Fuzzy Neural Network Smelting Process Bidirectional Associative Memory Converter Slag Precise Mathematical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Astron K J (1986) Expert control. Automatic, 22(3): 277–286CrossRefGoogle Scholar
  2. Cai Zixing, Xu Guangyou (2004) Artificial Intelligence: Principles and Applications (in Chinese) (Third Edition). Tsinghua University Press, BeijingGoogle Scholar
  3. Deng Zhidong (1994) Several representative structures and classification methods of neural network control system (in Chinese). In: Proceeding of The 2nd National Conference of Intelligent Control Exporters Google Scholar
  4. Fu K S (1971) Learning control systems and intelligent control systems: an intersection of artificial intelligence and automatic control. IEEE Trans, AC-16(1): 70–72Google Scholar
  5. Fu K S et al (1965) A heuristic approach to reinforcement learning control system. IEEE Trans. Automatic Control, 10(4): 390–398CrossRefGoogle Scholar
  6. Hebb D O (1949) The Organization of Behavior. Science Editions. New York: WileyGoogle Scholar
  7. Hecht Nielsen R (1987) Counter propagation networks. Applied Optics, 26(12): 4979–4984CrossRefGoogle Scholar
  8. Hinton G E, Sejnowski T J (1986) Learning and relearning in Boltzmann machines. In D. E. Rumelhart and J. L. McClelland (Eds.). Parallel Distributed Processing, 1(7)Google Scholar
  9. Hopfield J J (1982) Neural networks and physical systems with emergent collective computational abilities. Proc. of the National Academy of Science, U. S. A., 79: 2554–2558CrossRefGoogle Scholar
  10. Hopfield J J (1984) Neural with graded response have collective computational properties like those of two-state neurons. Proc. of the National Academy of Science, U. S. A., 81:3088–3092CrossRefGoogle Scholar
  11. Hu Shouren (1993) Application technology of neural network (in Chinese). National University of Defense Technology Press, ChangshaGoogle Scholar
  12. Joseph C G, Gary D R (2006) Expert Systems: Principles and Programming, Fourth Edition (in Chinese). China Machine Press, BeijingGoogle Scholar
  13. Kosko B (1987) Adaptive bidirectional associative memories. Applied Optics, 26(23): 4947–4960CrossRefGoogle Scholar
  14. Kosko B (1988) Bidirectional associative memories. IEEE Trans, SMC-18: 49–60Google Scholar
  15. Kosko B (1992a) Fuzzy function approximation. IJCNN'92, Baltimore, 1: 209–213Google Scholar
  16. Kosko B (1992b) Fuzzy system as universal approximators. IEEE Fuzzy'92: 1153–1162Google Scholar
  17. Li Anhua (1986) Groundwork and Application of Fuzzy Mathematics (in Chinese). Xinjiang People Press, XinjiangGoogle Scholar
  18. Li Shiyong (2004) Fuzzy Control, Neural Control and Intelligent Control Theory (in Chinese) (Second Edition). Harbin Institute of Technology Press, HarbinGoogle Scholar
  19. Li Youshan (1993) Fuzzy Control Theory and its Application in Process Control (in Chinese). National Defence Industry Press, BeijingGoogle Scholar
  20. Liu Zengliang (1997) Fuzzy Technology and its Application (in Chinese). Beijing University of Aeronautics & Astronautics Press, BeijingGoogle Scholar
  21. Mamdani E H (1974) Applications of fuzzy algorithms for control of simple dynamic plant. Proceedings of IEEE, 121(12): 1585–1588Google Scholar
  22. Mei Chi, Peng Xiaoqi, Zhou Jiemin (1994a) An intelligent decision support system on the process of nickel matte smelter. Journal of Central South University of Technology, 1(1)Google Scholar
  23. Mei Chi, Peng Xiaoqi, Zhou Jiemin (1994b) Fuzzy and adaptive control model for process in nickel matte smelting furnace. Transactions of Nonferrous Metals Society of China, 4(3)Google Scholar
  24. Peng Xiaoqi (1998) The development and application of the intelligent decision technique for economizing electric energy and reducing consumption in nickel smelting and the integrated computer information network in smelting workshop (in Chinese). Thesis (Ph. D). Central South University of Technology, ChangshaGoogle Scholar
  25. Peng Xiaoqi, Mei Chi, Zhou Jiemin (1996) An intelligent decision support system in the operation process of electric furnace for cleaning slag. Journal of Central South University of Technology, 3(2)Google Scholar
  26. Peng Xiaoqi, Mei Chi, Zhou Jiemin et al (1994) An identification method of multivariable fuzzy control model and its application in the decision support system of smelting furnace (in Chinese). Control Theory & Application, 11(5): 582–587Google Scholar
  27. Peng Xiaoqi, Mei Chi, Zhou Jiemin et al (1995) A fuzzy neural network decision model on the operation process of electric furnace for cleaning slag and its application. Transactions of Nonferrous Metals society of China, 5(3): 21–24Google Scholar
  28. Peng Xiaoqi, Su Daixiong, Mei Chi (1996) A dynamic fuzzy optimal decision model for the process of slag-cleaning electric furnace (in Chinese). Journal of Central South University of Technology (Natural Science edition), 27(6)Google Scholar
  29. Peng Xiaoqi, Zhou Jiemin, Mei Chi et al (1993) The fuzzy adaptive control of nickel matte smelting furnace (in Chinese). Journal of Central South Institute of Mining and Metallurgy, 24(6): 766–770Google Scholar
  30. Procky T J, Mamdani E H (1979) A linguistic self-organizing process controller. Automatic, 15(1): 15–30CrossRefGoogle Scholar
  31. Saridis G N (1977) Self-organizing Control of Stochastic Systems. Marcel Dekker Inc, New YorkGoogle Scholar
  32. Tomohiro Takagi, Michio Sugeno (1985). Fuzzy identification of system and its application to modeling and control. IEEE Transaction on Systems, Man and Cybernetics, SMC-15(1): 116–132Google Scholar
  33. Wu Guangyu (1987) System Identification and Adaptive Control (in Chinese). Harbin University of Technology Press, HarbinGoogle Scholar
  34. Wang Lixin (1992) Fuzzy system are universal approximators. IEEE Fuzzy'92: 1163–1170Google Scholar
  35. Wang Xuehui (1987) Theory and Application of Micro-computer Fuzzy Control (in Chinese). Electronic Industry Press, BeijingGoogle Scholar
  36. Wang Zhenhuai (1994) Fuzzy control and expert system (in Chinese). Automation and Instrumentation, 9(1):1–3Google Scholar
  37. Werbos P J (1974) Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Science. Thesis (Ph. D.). Appl. Math. Harvard UniversityGoogle Scholar
  38. Zadeh L A (1965) Fuzzy Sets. Information and Control, 8: 338–353CrossRefGoogle Scholar
  39. Zadeh L A (1973) Outline of a new approach to the analysis of complex systems and decision process. IEEE Trans on Sys, Man and Cobern, 1: 28–44Google Scholar
  40. Zadeh L A (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Information Sciences, 8: 199–249CrossRefGoogle Scholar
  41. Zhang Liangjie, Li Yanda (1995) The development and prospect on fuzzy neural network technology for intelligent control (in Chinese). Acta Electronica Sinica, 23(8): 65–69Google Scholar
  42. Zhang Zaixing, Sun Zengqi (1995) On expert control (in Chinese). Information and Control, 24(3): 167–172Google Scholar
  43. Zhang Zhenyu (1996) Groundwork and Application of Fuzzy Theory and Neural Network (in Chinese). Tsinghua University Press, BeijingGoogle Scholar
  44. Zhou Jicheng (1993) Artificial Neural Network (in Chinese). Science Popularization Press, BeijingGoogle Scholar

Copyright information

© Metallurgical Industry Press, Beijing and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Xiaoqi Peng
    • 1
  • Yanpo Song
    • 1
  1. 1.Central South UniversityChina

Personalised recommendations