A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven m...

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Bibliographic Details
Main Authors: Yanjiao Li, Sen Zhang, Yixin Yin, Wendong Xiao, Jie Zhang
Format: Article
Language:English
Published: MDPI AG 2017-08-01
Series:Sensors
Subjects:
soft-sensor approach
data-driven model
machine learning
gas utilization ratio
blast furnace
online sequential extreme learning machine
Online Access:https://www.mdpi.com/1424-8220/17/8/1847

Internet

https://www.mdpi.com/1424-8220/17/8/1847

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