Data-driven soft sensors in blast furnace ironmaking: a survey
The blast furnace is a highly energy-intensive, highly polluting, and extremely complex
reactor in the ironmaking process. Soft sensors are a key technology for predicting molten …
reactor in the ironmaking process. Soft sensors are a key technology for predicting molten …
Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace—A review
A review of black-box models for short-term time-discrete prediction of the silicon content of
hot metal produced in blast furnaces is presented. The review is primarily focused on work …
hot metal produced in blast furnaces is presented. The review is primarily focused on work …
A novel scheme for key performance indicator prediction and diagnosis with application to an industrial hot strip mill
In this paper, a data-driven scheme of key performance indicator (KPI) prediction and
diagnosis is developed for complex industrial processes. For static processes, a KPI …
diagnosis is developed for complex industrial processes. For static processes, a KPI …
Robust principal component pursuit for fault detection in a blast furnace process
Since blast furnaces are generally controlled by operators, the minor faults regarded as
disturbances might be contained in the collected data matrix. This can severely affect …
disturbances might be contained in the collected data matrix. This can severely affect …
Constructing multiple kernel learning framework for blast furnace automation
L Jian, C Gao, Z **a - IEEE Transactions on Automation …, 2012 - ieeexplore.ieee.org
This paper constructs the framework of the reproducing kernel Hilbert space for multiple
kernel learning, which provides clear insights into the reason that multiple kernel support …
kernel learning, which provides clear insights into the reason that multiple kernel support …
Fault detection with improved principal component pursuit method
In modern industries, principal component analysis (PCA) is one of the most popular data-
driven methods. Since the directions of loading vectors can be severely affected by samples …
driven methods. Since the directions of loading vectors can be severely affected by samples …
Ensemble non-Gaussian local regression for industrial silicon content prediction
Z Ding, J Zhang, Y Liu - ISIJ International, 2017 - jstage.jst.go.jp
Due to the complicated characteristics of modeling data in industrial blast furnaces (eg,
nonlinearity, non-Gaussian, and uneven distribution), the development of accurate data …
nonlinearity, non-Gaussian, and uneven distribution), the development of accurate data …
Just-in-time correntropy soft sensor with noisy data for industrial silicon content prediction
K Chen, Y Liang, Z Gao, Y Liu - Sensors, 2017 - mdpi.com
Development of accurate data-driven quality prediction models for industrial blast furnaces
encounters several challenges mainly because the collected data are nonlinear, non …
encounters several challenges mainly because the collected data are nonlinear, non …
Graph-based method for fault detection in the iron-making process
Since the iron-making process is performed in complicated environments and controlled by
operators, observation labeling is difficult and time-consuming. Therefore, unsupervised …
operators, observation labeling is difficult and time-consuming. Therefore, unsupervised …
A nonuniform delay-coordinate embedding-based multiscale predictor for blast furnace systems
C Gao, Q Lin, J Ni, W Guo, Q Li - IEEE Transactions on Control …, 2020 - ieeexplore.ieee.org
Blast furnace is the main equipment in modern ironmaking industry. To achieve hot metal of
high quality, the temperature in the blast furnace rector, often indicated by the silicon content …
high quality, the temperature in the blast furnace rector, often indicated by the silicon content …