Applications of Evolutionary Computation and Artificial Intelligence in Metallurgical Industry

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


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.


Evolutionary computation Artificial intelligence Metallurgical industry 



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.


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© 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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