A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem. Issue 10 (27th July 2017)

• Record Type: Journal Article
• Authors: Chugh, Tinkle; Chakraborti, Nirupam; Sindhya, Karthik; Jin, Yaochu
• Is Part Of: Materials and manufacturing processes. Volume 32:Issue 10(2017)
• Page Start: 1172
• Page End: 1178
• Publication Date: 2017-07-27
• Subjects: Blast furnace -- data-driven optimization -- iron-making -- metamodeling -- model management -- multi-objective optimization -- Pareto optimality; Manufacturing processes -- Periodicals; Materials -- Periodicals; Manufactured Materials; 670.5
• DOI: 10.1080/10426914.2016.1269923
• Languages: English
• ISSNs: 1042-6914

Abstract

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.