Advertisement

Optimization of Parameters for Steel Recycling Process by Using Particle Swarm Optimization Algorithm

  • S. Allurkar BaswarajEmail author
  • M. Sreenivasa Rao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 949)

Abstract

In steel recycling process, a lot of scrap is generated and it is used widely for producing steel. Nearly 40% of the world steel is produced by scrap steel recycling process. The main issue in recycling process is producing a quality steel out of scrap with minimum energy consumption. The parameters which influence the recycling process are furnace temperature, sponge steel addition percentage, scrap steel composition, TDS number of water used, and quenching temperature of steel. In this paper, an optimization model for maximizing tensile strength and hardness number value as objective function and energy consumption rate as a constraint is obtained by using response surface methodology. The detailed optimization model is solved by using state-of-the-art optimization technique called particle swarm optimization algorithm. Optimization values obtained are evaluated using experimentally and compared with other optimization techniques like Grey Taguchi. The optimum results obtained by particle swarm optimization (PSO) method outperform other techniques.

Keywords

Input parameters Steel recycling Tensile strength Swarm 

References

  1. 1.
    Khoeai, A.R.: Design optimization of steel recycling processes using Taguchi method. J. Mater. Process. Technol. 127(1), 97–106 (2007)Google Scholar
  2. 2.
    Patel, V.K., Rao, R.V.:Design optimization of shell and tube heat exchanger by PSO method. Appl. Thermal Eng. 30(11–12), 1417–1425 (2010)Google Scholar
  3. 3.
    Anil, G., Singh,H., Aggarwal, A.: Taguchi method for multi output optimization high speed CNC turning of AISI steel. Expert Syst. Appl. 38(6), 6823–6827 (2011)Google Scholar
  4. 4.
    Haupt, M., Vadenbo, C.: Influence of input-scrap quality on the environmental impact for secondary steel. J. Ind. Ecol 21(2), 391–401 (2017)Google Scholar
  5. 5.
    Feng, M., Yi, X.: Grouping particle swarm optimization algorithm for job shop problem with constraints.In: 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Application, pp. 332–335Google Scholar
  6. 6.
    Liu, Z.: Investigation of particle swarm optimization for job shop scheduling problem. In: Third International Conference on Natural Computation (2007)Google Scholar
  7. 7.
    Kennedi, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1941–1949 (1995)Google Scholar
  8. 8.
    Zhu, H., Ye, W.: A particle Swarm optimization for integrated process planning and scheduling. In: IEEE Proceedings, pp. 1070–1074 (2009)Google Scholar
  9. 9.
    Xu, X.-H., Zeng, L.-L.: Hybrid particle Swarm optimization for flexible job shop scheduling problem and its implementation. In: Proceedings of 2010 IEEE International Conference on Information and Automation, pp. 1155–1159Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.JNTU College of EngineeringHyderabadIndia

Personalised recommendations