A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem
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.
Main Authors
Format
Articles
Research article
Published
2017
Series
Subjects
Publication in research information system
Publisher
Taylor & Francis Inc.
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-201707183318Use this for linking
Review status
Peer reviewed
ISSN
1042-6914
DOI
https://doi.org/10.1080/10426914.2016.1269923
Language
English
Published in
Materials and Manufacturing Processes
Citation
- Chugh, T., Chakraborti, N., Sindhya, K., & Jin, Y. (2017). A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem. Materials and Manufacturing Processes, 32(10), 1172-1178. https://doi.org/10.1080/10426914.2016.1269923
License
Copyright© 2017 Taylor & Francis. This is a final draft version of an article whose final and definitive form has been published by Taylor & Francis. Published in this repository with the kind permission of the publisher.