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

作者：Chugh Tinkle^*; Chakraborti Nirupam; Sindhya Karthik; Jin Yaochu

来源：Materials and Manufacturing Processes, 2017, 32(10): 1172-1178.

DOI：10.1080/10426914.2016.1269923

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

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.

• 出版日期2017