A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem |
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| Authors: | Tinkle Chugh Nirupam Chakraborti Karthik Sindhya Yaochu Jin |
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| Affiliation: | 1. Faculty of Information Technology, University of Jyv?skyl?, Jyv?skyl?, Finlandtinkle.chugh@jyu.fi;3. Department of Metallurgical &4. Materials Engineering, Indian Institute of Technology Kharagpur, India;5. Faculty of Information Technology, University of Jyv?skyl?, Jyv?skyl?, Finland;6. Department of Computer Science, University of Surrey, Guildford, United Kingdom |
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| 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. |
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| Keywords: | Blast furnace data-driven optimization iron-making metamodeling model management multi-objective optimization Pareto optimality |
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