Blast furnace raw material granularity recognition model based on deep learning and multimodal fusion of 3D point cloud
Author:
Funder
Tangshan city applied basic research science and technology plan project
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03449-5.pdf
Reference27 articles.
1. Lou, S.W., Yang, C.J., Zhu, X.Z., Zhang, H.W., Wu, P.: Adaptive dynamic inferential analytic stationary subspace analysis: a novel method for fault detection in blast furnace ironmaking process. Inform Sciences 642, 1191760 (2023)
2. Perpiñán, J., Bailera, M., Peña, B., Romeo, L.M., Eveloy, V.: High oxygen and SNG injection in blast furnace ironmaking with power to gas integration and CO2 recycling. J. Clean. Prod. 405, 137001 (2023)
3. Rahmatmand, B., Tahmasebi, A., Lomas, H., Honeyands, T., Koshy, P., Hockings, K.: A technical review on coke rate and quality in low-carbon blast furnace ironmaking. Fuel 336, 127077 (2023)
4. Chen, J.W., Zhang, H., Zhao, G., Samad, A.: A novel method for estimating carbon emission based on industrial metabolism: blast furnace iron-making with micro mechanism model. Energy Rep. 8, 10125–10133 (2022)
5. Gao, X.D., Zhang, R., You, Z.X., Yu, W.Z., Dang, J.: Use of hydrogen-rich gas in blast furnace ironmaking of V–bearing titanomagnetite: mass and energy balance calculations. Materials 15(17), 6078 (2022)
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