Endpoint carbon content and temperature prediction model in BOF steelmaking based on posterior probability and intra-cluster feature weight online dynamic feature selection
A posterior probability and intra-cluster feature weight online dynamic feature selection algorithm is proposed to address the issues of high dimensionality and high volatility of data in the basic oxygen furnace (BOF) steelmaking production process. First, a genetic algorithm with fixed feature spa...
Saved in:
Main Authors: | Wang Haodong, Liu Hui, Chen FuGang, Li Heng, Xue XiaoJun |
---|---|
Format: | Article |
Language: | English |
Published: |
De Gruyter
2025-01-01
|
Series: | High Temperature Materials and Processes |
Subjects: | |
Online Access: | https://doi.org/10.1515/htmp-2024-0067 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The application of citric acid solutions for selective removal of zinc from steelmaking dust
by: Gargul K., et al.
Published: (2021-01-01) -
Recycling of steelmaking dusts: The Radust concept
by: Jalkanen H., et al.
Published: (2005-01-01) -
Reduction of molybdenum oxide from steelmaking slags by pure liquid iron
by: Gao Y.M., et al.
Published: (2012-01-01) -
Prediction of end-point phosphorus content of molten steel in BOF with machine learning models
by: Kang Y., et al.
Published: (2024-01-01) -
Influence of lance height and angle on the penetration depth of inclined coherent and conventional supersonic jets in electric arc furnace steelmaking
by: Wu X.-T., et al.
Published: (2020-01-01)