Prediction of Multiple Molten Iron Quality Indices in the Blast Furnace Ironmaking Process Based on Attention-wise Deep Transfer Network

Molten iron quality (MIQ) indices prediction based on data-driven models is an important way to monitor product quality and smelting status in the blast furnace ironmaking process. However, some challenges still place in the MIQ prediction: 1) limited nonlinear and dynamic description capabilities a...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 71; S. 1
Hauptverfasser:	Jiang, Ke, Jiang, Zhaohui, Xie, Yongfang, Pan, Dong, Gui, Weihua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Attention-wise deep network Blast furnace Computational modeling Denoising autoencoder (DAE) Heuristic algorithms Iron Ironmaking Modules Molten iron quality Multitask transfer learning Prediction Predictive models Process variables Silicon Smelting Task analysis
ISSN:	0018-9456, 1557-9662
Online-Zugang:	Volltext
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