6533b7cffe1ef96bd12598f2
RESEARCH PRODUCT
A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem
Nirupam ChakrabortiKarthik SindhyaYaochu JinTinkle Chughsubject
data-driven optimizationPareto optimalityEngineeringBlast furnaceMathematical optimizationOptimization problemmodel managementblast furnaceEvolutionary algorithm02 engineering and technologyMulti-objective optimizationIndustrial and Manufacturing Engineering020501 mining & metallurgyData-drivenironmakingoptimointi0202 electrical engineering electronic engineering information engineeringGeneral Materials Scienceta113business.industrypareto-tehokkuusMechanical EngineeringProcess (computing)metamodelingMetamodeling0205 materials engineeringmulti-objective optimizationMechanics of MaterialsPrincipal component analysis020201 artificial intelligence & image processingbusinessrautateollisuusdescription
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. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-17 |