期刊论文详细信息
IEEE Access | 卷:9 |
Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II | |
Ashraf A. Fahmy1  Matheus F. Torquato1  Johann Sienz1  German Martinez-Ayuso2  | |
[1]Advanced Sustainable Manufacturing Technologies 2020, Swansea University, Swansea, U.K. | |
[2]|Medical School, Swansea University, Swansea, U.K. | |
关键词: Electric arc furnace; genetic algorithms; multi-objective optimisation; NSGA-II; optimisation; steel-making; | |
DOI : 10.1109/ACCESS.2021.3125519 | |
来源: DOAJ |
【 摘 要 】
Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective optimisation using evolutionary computing and machine learning. Using a large amount of real-world historical data containing 6423 consecutive EAF heats collected from a melt shop in an established steel plant this work not only creates machine learning models for both EAF and ladle furnaces but also simultaneously minimises the total scrap cost and EAF energy consumption per ton of scrap. In the modelling process, several algorithms are tested, tuned, evaluated and compared before selecting Gradient Boosting as the best option to model the data analysed. A similar approach is followed for the selection of the multi-objective optimisation algorithm. For this task, six techniques are tested and compared based on the hypervolume performance indicator to just then select the Non-dominated Sorting Genetic【 授权许可】
Unknown