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Evolutionary Computation in Blast Furnace Iron Making

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Optimization in Industry

Part of the book series: Management and Industrial Engineering ((MINEN))

Abstract

Various methods and models are used to optimize the different aspects of blast furnace iron-making process involving the quality of hot metal, productivity, and the cost of production. Analytical models which are used to solve such problems are often not adequate to obtain the optimized results. Nowadays data-driven models are used effectively for this purpose. In this chapter, various soft computing techniques, with a special emphasis on evolutionary computation methods are presented to elaborate their implementation in different areas of blast furnace iron making. The input–output data models and simulation results are discussed with respect to various aspects of ferrous production metallurgy like sintering process, gas scheduling , prediction and process control of hot metal, temperature prediction of hot metal, silicon content in the hot metal, burden and gas distribution, carbon dioxide emission, performance of rotary kiln, coal injection, biomass injection, and many objective optimization problems, etc. The results and comparison between various models are thoroughly analyzed and a discussion regarding these data-driven models and their influence in the blast furnace iron-making process is presented in a comprehensive manner.

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Authors and Affiliations

  1. Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Kharagpur, India

    Bashista Kumar Mahanta & Nirupam Chakraboti

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  1. Bashista Kumar Mahanta
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  2. Nirupam Chakraboti
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Editors and Affiliations

  1. Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai, India

    Shubhabrata Datta

  2. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    J. Paulo Davim

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Mahanta, B.K., Chakraboti, N. (2019). Evolutionary Computation in Blast Furnace Iron Making. In: Datta, S., Davim, J. (eds) Optimization in Industry. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-01641-8_8

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  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01640-1

  • Online ISBN: 978-3-030-01641-8

  • eBook Packages: EngineeringEngineering (R0)

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