A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem

ABSTRACT

A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process.

KEYWORDS:

• Blast furnace
• data-driven optimization
• iron-making
• metamodeling
• model management
• multi-objective optimization
• Pareto optimality

Acknowledgments

This research reported here was supported by FiDiPro project DeCoMo funded by TEKES, The Finnish Funding Agency for Innovation. The authors very much appreciate several suggestions of Professor Kaisa Miettinen, which significantly enhanced the quality of this work.