"Data Mining and Machine Learning to Predict the Sulphur Content in the Hot ..."

Wandercleiton Cardoso, Renzo di Felice (2022)

> Home

Details and statistics

DOI: 10.1007/978-3-031-37890-4_10

access: closed

type: Conference or Workshop Paper

metadata version: 2023-09-14

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/data/CardosoF22a
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/data/CardosoF22a
Wandercleiton Cardoso, Renzo di Felice:
Data Mining and Machine Learning to Predict the Sulphur Content in the Hot Metal of a Coke-Fired Blast Furnace. DATA (Revised Selected Papers) 2022: 203-218

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.