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

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Blast furnace Evolutionary algorithm Principal component analysis Process (computing) Construct (python library) Surrogate model Computer science Mathematical optimization Algorithm Materials science Mathematics Metallurgy Artificial intelligence Operating system Programming language

Tinkle Chugh , Nirupam Chakraborti , Karthik Sindhya , Yaochu Jin ·

YOU? · · 2016 · Open Access · · DOI: https://doi.org/10.1080/10426914.2016.1269923 · OA: W2566693540

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.