Abstract
The burden distribution matrix is an important means of controlling the burden distribution of blast furnace. In order to provide references to foremen of blast furnaces, a model is established in this paper, which is a relationship model between blast furnace parameters and burden distribution matrix, and is based on the extreme learning machine algorithm (ELM). The model decides if the next burden distribution matrix should be adjusted through a series of blast furnace parameters, such as the gas utilization rate, the blast volume, the blast pressure, the top pressure, the blast velocity, the permeability index, and the utilization coefficient. Finally, compared to other methods used LSSVM and PNN, the method based on ELM is faster and more accurate, so it is more suitable.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (NSFC Grant No. 61333002 and No. 61673056).
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Liu, Y., Zhang, S., Yin, Y., Su, X., Dong, J. (2019). Classification of Burden Distribution Matrix Based on ELM. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM-2017. ELM 2017. Proceedings in Adaptation, Learning and Optimization, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-01520-6_20
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DOI: https://doi.org/10.1007/978-3-030-01520-6_20
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