A Novel Method to Determine Desired PCI Rate for Ensuring Thermal Stability in a Blast Furnace
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s40831-024-00902-6.pdf
Reference19 articles.
1. Spanlang A, Wukovits W, Weiss B (2020) Development of a blast furnace model with thermodynamic process depiction by means of the rist operating diagram. Berg Huettenmaenn Monatsh 165:243–247. https://doi.org/10.1007/s00501-020-00963-6
2. Pustějovská P, Bilík J, Jursová S, Kardas E, Konstanciak A (2023) Prediction of the consumption of raw materials and fuels for the blast furnace. Processes 11:79. https://doi.org/10.3390/pr11010079
3. Cardoso W, di Felice R, Baptista R. Mathematical modelling to predict fuel consumption in a blast furnace using artificial neural networks. In: García Márquez FP (ed) International conference on intelligent emerging methods of artificial intelligence & cloud computing. Smart innovation, systems and technologies, vol 273. https://doi.org/10.1007/978-3-030-92905-3_1
4. Bhattacharjee A, Chattopadhyaya S (2022) Carbon rate prediction model using artificial neural networks (ANN). In: Misra R, Kesswani N, Rajarajan M, Veeravalli B, Patel A (eds) Internet of things and connected technologies. ICIoTCT 2021. Lecture notes in networks and systems, vol 340. Springer, Cham. https://doi.org/10.1007/978-3-030-94507-7_8
5. Bernasowski M, Klimczyk A, Stachura R (2019) Support algorithm for blast furnace operation with optimal fuel consumption. J Min Metall Sect B Metall 55(1):34. https://aseestant.ceon.rs/index.php/jmm/article/view/16468
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