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Applications of Evolutionary Computation and Artificial Intelligence in Metallurgical Industry

  • Jianqi AnEmail author
  • Jinhua She
  • Huicong Chen
  • Min Wu
Chapter
Part of the Communications in Computer and Information Science book series (CCIS, volume 999)

Abstract

Metallurgical industry is one of the most important industrial processes, which mainly consists of coking process, sintering process, ironmaking process, and casting and rolling process. All of the metallurgical processes are complex, multivariate and nonlinear systems with large time-delay. Some chemical or physical mechanisms are even not clear and uncertain. It is difficult to establish the models, design the controllers, devise the scheduling and optimization strategies, and make the operation decisions by the conventional mechanism-based methods. Nevertheless, these processes work continuously and repetitively, which produces large amounts of data, and consists of lots of knowledge and expert experiences. In the last decade, evolutionary computation and artificial intelligence (ECAI) began to be widely used in metallurgical industry and many good results were reported. This letter demonstrates how the development of ECAI impacts the metallurgical industry by analyzing some good applications of the ECAI in typical metallurgical processes and discusses the future development trends and challenges of the applications of the ECAI in metallurgical industries.

Keywords

Evolutionary computation Artificial intelligence Metallurgical industry 

Notes

Acknowledgement

This work is supported by Hubei Provincial Natural Science Foundation of China under Grants 2016CFB480 and 2015CFA010, National Natural Science Foundation of China under Grants 61333002 and 61203017, the Foundation Research Founds for China University of Geosciences under Grant 2015349120, and the 111 project under Grant B17040. The first author is an overseas researcher under Postdoctoral Fellowship of Japan Society for the Promotion of Science (JSPS), and his JSPS Fellowship ID is P16799.

References

  1. 1.
    Chertov, A.D.: Application of artificial intelligence systems in metallurgy. Metallurgy 7, 32–37 (2003)Google Scholar
  2. 2.
    Yin, C., Luo, Z., Zhou, J., et al.: A novel non-linear programming-based coal blending technology for power plants. Chem. Eng. Res. Des. 78(1), 118–124 (2000)CrossRefGoogle Scholar
  3. 3.
    Xie, N., Cheng, S.: Analysis of effect of gas temperature on cooling stave of blast furnace. J. Iron Steel Res. 17(1), 1–6 (2010)CrossRefGoogle Scholar
  4. 4.
    Martín, R.D., Obeso, F., Mochón, J., et al.: Hot metal temperature prediction in blast furnace using advanced model based on fuzzy logic tools. Ironmaking Steelmaking 34(3), 241–247 (2007)CrossRefGoogle Scholar
  5. 5.
    Bilim, C., Ati, C.D., Tanyildizi, H., et al.: Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network. Adv. Eng. Softw. 40(5), 334–340 (2009)CrossRefGoogle Scholar
  6. 6.
    Cierpisz, S., Heyduk, A.: A simulation study of coal blending control using a fuzzy logic ash monitor. Control Eng. Pract. 10(4), 449–456 (2002)CrossRefGoogle Scholar
  7. 7.
    Liao, Y., She, J., Wu, M.: Integrated hybrid-PSO and fuzzy-NN decoupling control for temperature of reheating furnace. IEEE Trans. Industr. Electron. 56(7), 2704–2714 (2009)CrossRefGoogle Scholar
  8. 8.
    Li, M., Wang, Q., Sun, Y.: Sintering blending optimization based on hybrid particle swarm algorithm. Inf. Control 37(2), 242–246 (2008)Google Scholar
  9. 9.
    Zhang, J., Xie, A., Shen, F.: Multi-objective optimization and analysis model of sintering process based on BP neural network. Int. J. Iron Steel Res. 14(2), 1–5 (2007)CrossRefGoogle Scholar
  10. 10.
    Wu, M., Chen, X., Cao, W., et al.: An intelligent integrated optimization system for the proportioning of iron ore in a sintering process. J. Process Control 24(1), 182–202 (2014)CrossRefGoogle Scholar
  11. 11.
    Kim, B.R., Jeong, J.W., Hwang, K., et al.: Estimation of burn-through point in the sinter process. In: Proceeding of 14th International Conference on Control. Automation and Systems, pp. 1531–1533. IEEE, South Korea (2014)Google Scholar
  12. 12.
    Wu, M., Xu, C., She, J., et al.: Intelligent integrated optimization and control system for lead-zinc sintering process. Control Eng. Pract. 17(2), 280–290 (2009)CrossRefGoogle Scholar
  13. 13.
    Xiang, J., Wu, M. Duan, P., et al.: Coordinating fuzzy control of the sintering process. In: Proceeding of 17th IFAC World Congress, pp. 7717–7722. Elsevier, Seoul (2008)Google Scholar
  14. 14.
    Chen, X., Hu, J., Wu, M., et al.: T-S fuzzy logic based modeling and robust control for burning-through point in sintering process. IEEE Trans. Industr. Electron. 99, 9378–9388 (2017)CrossRefGoogle Scholar
  15. 15.
    Wu, M., Cao, W., Chen, X., et al.: Intelligent optimization and control of complex metallurgical processes. Springer, in pressingGoogle Scholar
  16. 16.
    Chen, X., Chen, X., She, J., et al.: A hybrid just-in-time soft sensor for carbon efficiency of iron ore sintering process based on feature extraction of cross-sectional frames at discharge end. J. Process Control 54, 14–24 (2017)CrossRefGoogle Scholar
  17. 17.
    Wang, C., Wu, M.: Hierarchical intelligent control system and its application to the sintering process. IEEE Trans. Industr. Inf. 9(1), 190–197 (2012)CrossRefGoogle Scholar
  18. 18.
    Wu, M., Duan, P., Cao, W., et al.: 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), 5971–5981 (2012)CrossRefGoogle Scholar
  19. 19.
    Xiang, L., Wu, M., Xiang, J.: A fuzzy sliding Model Control Strategy for the Burning through point and its application in sintering process. J. East China Univ. Sci. Technol. 32(7), 820–836 (2006)Google Scholar
  20. 20.
    Chakraborty, A., Chakraborty, M.: Multi criteria genetic algorithm for optimal blending of coal. Opsearch 49(4), 386–399 (2012)CrossRefGoogle Scholar
  21. 21.
    Deng, J., Lai, X., Wu, M., et al.: Intelligent optimization method for coal blending based on neural network and simulated annealing algorithm. Metall. Ind. Autom. 31(3), 19–23 (2007)Google Scholar
  22. 22.
    Lei, Q., Yu, H., Wu, M., et al.: Modeling of complex industrial process based on active semi-supervised clustering. Eng. Appl. Artif. Intell. 56, 131–141 (2016)CrossRefGoogle Scholar
  23. 23.
    Wu, M., Lei, Q., Cao, W., et al.: Integrated soft sensing of coke-oven temperature. Control Eng. Pract. 19(10), 1116–1125 (2011)CrossRefGoogle Scholar
  24. 24.
    Lei, Q., Wu, M., She, J.: Online optimization of fuzzy controller for coke-oven combustion process based on dynamic just-in-time learning. IEEE Trans. Autom. Sci. Eng. 12(4), 1535–1540 (2015)CrossRefGoogle Scholar
  25. 25.
    Wu, M., Yan, J., She, J., et al.: Intelligent decoupling control of gas collection process of multiple asymmetric coke ovens. IEEE Trans. Industr. Electron. 56(7), 2782–2792 (2009)CrossRefGoogle Scholar
  26. 26.
    Zhang, R., Tao, J., Gao, F.: A new approach of takagi-sugeno fuzzy modeling using an improved genetic algorithm optimization for oxygen content in a coke furnace. Industr. Eng. Chem. Res. 55, 6465–6474 (2016)CrossRefGoogle Scholar
  27. 27.
    An, J., Yang, J., Wu, M.: Decoupling control method with fuzzy theory for top pressure of blast furnace. IEEE Trans. Control Syst. Technol.  https://doi.org/10.1109/TCST.2018.2862859
  28. 28.
    Zhao, J., Wang, W., Liu, Y., et al.: A two-stage online prediction method for a blast furnace gas system and its application. IEEE Trans. Control Syst. Technol. 19(3), 507–520 (2011)CrossRefGoogle Scholar
  29. 29.
    Lv, Z., Zhao, J., Liu, Y., et al.: Use of a quantile regression based echo state network ensemble for construction of prediction Intervals of gas flow in a blast furnace. Control Eng. Pract. 46, 94–104 (2016)CrossRefGoogle Scholar
  30. 30.
    Zhao, J., Liu, Q., Pedrycz, W., et al.: Effective noise estimation-based online prediction for byproduct gas system in steel industry. IEEE Trans. Industr. Inf. 8(4), 953–963 (2012)CrossRefGoogle Scholar
  31. 31.
    An, J., Zhang, J., Wu, M., et al.: Soft-sensing method for slag-crust state of blast furnace based on two-dimensional decision fusion. Neurocomputing 315, 405–411 (2018)CrossRefGoogle Scholar
  32. 32.
    Li, J., Hua, C., Yang, Y., et al.: Bayesian block structure sparse based T-S fuzzy modelling for dynamic prediction of hot metal silicon content in the blast furnace. IEEE Trans. Industr. Electron. 65(6), 4933–4942 (2018)CrossRefGoogle Scholar
  33. 33.
    Hua, C., Wu, J., Li, J., et al.: Silicon content prediction and industrial analysis on blast furnace using support vector regression combined with clustering algorithms. Neural Comput. Appl. 28(12), 4111–4121 (2017)CrossRefGoogle Scholar
  34. 34.
    Zhou, B., Ye, H., Zhang, H.F., Li, M.L.: Process monitoring of iron-making process in a blast furnace with PCA-based methods. Control Eng. Pract. 47, 1–14 (2016)CrossRefGoogle Scholar
  35. 35.
    Gao, C., Ge, Q., Jian, L.: Rule extraction from fuzzy-based blast furnace SVM multiclassifier for decision-making. IEEE Trans. Fuzzy Syst. 22(3), 586–596 (2014)CrossRefGoogle Scholar
  36. 36.
    Su, X., Zhang, S., Yin, Y., et al.: Data-driven prediction model for adjusting burden distribution matrix of blast furnace based on improved multilayer extreme learning machine. Soft Comput. 22(11), 3575–3589 (2018)CrossRefGoogle Scholar
  37. 37.
    Wu, M., Zhang, K., An, J., et al.: An energy efficient decision-making strategy of burden distribution for blast furnace. Control Eng. Pract. 78, 186–195 (2018)CrossRefGoogle Scholar
  38. 38.
    Zhang, J., Xie, A., Shen, F.: A hybrid intelligent system for PID controller using in a steel rolling process. Expert Syst. Applicat. 40(13), 5188–5196 (2013)CrossRefGoogle Scholar
  39. 39.
    Bagheripoor, M., Bisadi, H.: Application of artificial neural networks for the prediction of roll force and roll torque in hot strip rolling process. Appl. Math. Model. 37(7), 4593–4607 (2013)CrossRefGoogle Scholar
  40. 40.
    Lee, D., Lee, Y.: Application of neural-network for improving accuracy of roll-force model in hot-rolling mill. Control Eng. Pract. 10, 473–478 (2002)CrossRefGoogle Scholar
  41. 41.
    Shardt, Y.A.W., Mehrkanoon, S., Zhang, K., et al.: Modelling the strip thickness in hot steel rolling mills using least-squares support vector machines. Can. J. Chem. Eng. 96, 171–178 (2018)CrossRefGoogle Scholar
  42. 42.
    Li, S., Chen, Q., Huang, G.B.: Dynamic temperature modeling of continuous annealing furnace using GGAP-RBF neural network. Neurocomputing 69, 523–536 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of AutomationChina 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

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