• Min WuEmail author
  • Weihua Cao
  • Xin Chen
  • Jinhua She
Part of the Engineering Applications of Computational Methods book series (EACM, volume 3)


The iron and steel industry is the base for the development of national economy. It directly influences the industries of construction, machinery, shipbuilding, automobile, household electrical appliances, etc. The complexity and large uncertainties of the processes in the iron and steel industry make it difficult to establish accurate mathematical models and to control the processes using conventional control methods. On the other hand, since intelligent control does not require an accurate mathematics model, and its control algorithm has self-learning and adaptive ability, this control method is playing an increasingly important role in the iron and steel industry.


  1. 1.
    Li Y, Wu M, Cao WH, Lai XZ, Wang CS (2012) PSO-BP control algorithm of granulation process based on evaluation and optimization of granularity distribution. Acta Autom Sin 38(6):1007–1016 (In Chinese)CrossRefGoogle Scholar
  2. 2.
    Hou C, Yu X, Cao Y, Lai C, Cao Y (2018) Prediction of synchronous closing time of permanent magnetic actuator for vacuum circuit breaker based on PSO-BP. IEEE Trans Dielectr Electr Insul 24(6):3321–3326CrossRefGoogle Scholar
  3. 3.
    Wu M, Duan P, Cao WH, She JH, Xiang J (2012) An intelligent control system based on prediction of the burn-through point for the sintering process of an iron and steel plant. Expert Syst Appl 39(5):5971–5981CrossRefGoogle Scholar
  4. 4.
    Guan L (2006) An intelligent modeling method in Slab’s hot rolling process based on the rolling information feedback. Dalian University of Technology, Dalian (In Chinese)Google Scholar
  5. 5.
    Wu M, Cao WH, He CY, She JH (2009) Integrated intelligent control of gas mixing-and-pressurization process. IEEE Trans Control Syst Technol 17(1):68–77CrossRefGoogle Scholar
  6. 6.
    Li F, Ma X, Cao WH, Yuan Y (2011) Fuzzy compensation decoupling control of calorific value and pressure in gas mixing process. J Cent South Univ (Science and Technology) 42(1): 94-99 (In Chinese)Google Scholar
  7. 7.
    Deng J, Lai XZ, Wu M, Cao WH (2007) Intelligent optimization method for coal blending based on neural network and simulated annealing algorithm. Metall Ind Autom 3:19–23 (In Chinese)Google Scholar
  8. 8.
    Shang XQ, Lu JG, Sun YX, Liu J, Ying YQ (2010) Data-driven prediction of sintering burn-through point based on novel genetic programming. Int J Iron Steel Res 17(12):1–5CrossRefGoogle Scholar
  9. 9.
    Liao YX, She JH, Wu M (2009) Integrated hybrid-PSO and fuzzy-NN decoupling control for temperature of reheating furnace. IEEE Trans Ind Electron 56(7):2704–2714CrossRefGoogle Scholar
  10. 10.
    Wang XD, Wang ZF, Liu Y, Du FM, Yao M, Zhang XB (2016) A particle swarm approach for optimization of secondary cooling process in slab continuous casting. Int J Heat Mass Transf 93:250–256CrossRefGoogle Scholar
  11. 11.
    McClelland JL, Rumelhart DE (1988) Explorations in parallel distributed processing: a handbook of models, programs, and exercises. MIT Press, CambridgeGoogle Scholar
  12. 12.
    Simon H (2011) Neural networks and learning machines, 3rd edn. Prentice Hall PTR, Upper Saddle RiverGoogle Scholar
  13. 13.
    Fei J, Wang T (2018) Adaptive fuzzy-neural-network based on RBFNN control for active power filter. Int J Mach Learn Cybern. Scholar
  14. 14.
    Hui W, Xie W, Pei J (2015) A pre-radical basis function with deep back propagation neural network research. In: Proceedings of IEEE international conference on signal, pp 1489–1494Google Scholar
  15. 15.
    Yu S, Chen S, Zhang Z, Zhang Y (2018) A novel blind detection algorithm based on double sigmoid hysteretic chaotic hopfield neural network. J Inf Hiding Multimed Signal Process 9(2):452–460Google Scholar
  16. 16.
    Silva HO, Bastos-Filho CJA (2018) Inter-domain routing for communication networks using Hierarchical Hopfield Neural Networks. Eng Appl Artif Intell 70:184–198CrossRefGoogle Scholar
  17. 17.
    Zadeh LA (1965) Fuzzy sets, information and control. Inform Control 8(3): 338-353MathSciNetCrossRefGoogle Scholar
  18. 18.
    Li H, Wu C, Yin S, Lam HK (2016) Observer-based fuzzy control for nonlinear networked systems under unmeasurable premise variables. IEEE Trans Fuzzy Syst 24(5):1233–1245CrossRefGoogle Scholar
  19. 19.
    Oliveira KF, César MB, Gonçalves J (2017) Fuzzy based control of a vehicle suspension system using a MR damper. In: Proceedings of the 12th Portuguese confernece on automatic control, pp 571–581Google Scholar
  20. 20.
    Liao SH (2005) Expert system methodologies and applications–a decade review from 1995 to 2004. Expert Syst Appl 28(1):93–103CrossRefGoogle Scholar
  21. 21.
    Chen Q, Liu G, Cai X, Xu G (2018) Decoupling control of five-phase fault-tolerant permanent magnet motor by radial basis function neural network inverse. In: IEEE international magnetics conference, pp 1–2Google Scholar
  22. 22.
    Wu M, Yan J, She JH, Cao WH (2009) Intelligent decoupling control of gas collection process of multiple asymmetric coke ovens. IEEE Trans Ind Electron 56(7):2782–2792CrossRefGoogle Scholar
  23. 23.
    Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, CambridgeCrossRefGoogle Scholar
  24. 24.
    John HH (1975) Adaptation in natural and artifical systems. MIT Press, CombridgeGoogle Scholar
  25. 25.
    Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. In: Proceedings of European conference on artificial life, pp 134–142Google Scholar
  26. 26.
    Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66CrossRefGoogle Scholar
  27. 27.
    Chahal P, Singh J (2012) Study of mathematical model and ant colony optimization (ACO). Int J Educ Appl Res 2(1):101–105MathSciNetGoogle Scholar
  28. 28.
    Pirjanian P (1998) Satisficing action selection. In: SPIE conference on intelligent systems and advanced manufacturing, pp 153–164Google Scholar

Copyright information

© Science Press 2020

Authors and Affiliations

  1. 1.China University of GeosciencesWuhanChina
  2. 2.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhanChina
  3. 3.School of EngineeringTokyo University of TechnologyTokyoJapan

Personalised recommendations