Ensemble Non-Gaussian Local Regression for Industrial Silicon Content Prediction

Ding, Zhenyu; Zhang, Jie; Liu, Yi

doi:10.2355/isijinternational.isijint-2017-251

Cited by 13 publications

(11 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The silicon content control in the hot metal is a state-ofthe-art process. 24) For better understanding of the elementary reaction rates, the dissolution rate of SiO gas in liquid iron was investigated at 1 843, 1 868, and 1 893 K. The SiO gas was generated from the chemical reaction between silica and graphite particles. The partial pressure of SiO gas was estimated from the weight change of the silica-graphite mixture and the CO carrier gas flow rate.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Kinetics of Dissolution of SiO Gas in Liquid Fe–C Alloys

Kim

Lee

et al. 2018

ISIJ Int.

View full text Add to dashboard Cite

The dissolution rate of SiO gas in liquid iron (Fe-2 wt% C and Fe-4 wt% C alloys) was investigated at 1 843, 1 868, and 1 893 K. SiO gas was generated from a silica-graphite particle mixture, and then, it was introduced onto the surface of liquid iron through an alumina lance with CO carrier gas. The effect of gasphase mass transfer on the dissolution rate of SiO gas was minimized by adjusting the CO gas flow rate. The rate of silicon transfer remained almost constant regardless of the initial carbon content in liquid iron, whereas it increased with increasing temperature. From the experimental results, it was concluded that the adsorption of SiO gas onto the surface of liquid iron was the rate-determining step.

show abstract

Section: Discussionmentioning

confidence: 99%

“…If the dissociation of SiO on the liquid iron surface is the rate-limiting step, the reaction rate can be described by Eq. (24).…”

Section: Dissolution Of Sio In Liquid Ironmentioning

confidence: 99%

Kinetics of Dissolution of SiO Gas in Liquid Fe–C Alloys

Kim

Lee

et al. 2018

ISIJ Int.

View full text Add to dashboard Cite

show abstract

“…4) The Si content has also been successfully controlled by a state-space model 5) or a support vector regression model. 6) Although these thermal control systems based on transient models 7,8) or statistical models 9,10) are practical under normal operating conditions, full-automatic control that can cope with abnormal conditions has not been reported. In particular, the transient models suffer from large prediction errors when material characteristics, e.g., reducibility of iron ore, fluctuate.…”

Section: An Operator Behavior Model For Thermal Control Of Blast Furnacementioning

confidence: 99%

An Operator Behavior Model for Thermal Control of Blast Furnace

Hashimoto¹,

Masuda²,

Yasuhara³

2022

ISIJ Int.

View full text Add to dashboard Cite

“…HMT was predicted by a fuzzy inference system 8) or a data-based online model. 9) Silicon content of hot metal was predicted by a neural network model, 10) switching linear systems, 11) a non-Gaussian local regression, 12) a support vector regression, 13) or a just-in-time model. 14) Predictive control of silicon content with a neural network model 15) or a state-space model 16) was proposed.…”

Section: Introductionmentioning

confidence: 99%

Practical Operation Guidance on Thermal Control of Blast Furnace

Hashimoto¹,

Okamoto²,

Kaise³

et al. 2019

ISIJ Int.

View full text Add to dashboard Cite

Blast furnaces are still manually operated in the steel industry. To realize an efficient and stable operation, the authors developed an operation guidance system for controlling hot metal temperature and applied it to actual furnaces. The system features a newly developed 2D transient model that describes the sluggish and complicated process dynamics. Based on the transient model, nonlinear model predictive control (NMPC) and moving horizon estimation (MHE) were adopted to provide operators with appropriate control actions while minimizing the undesirable influence of disturbances. The online validation results demonstrated that the developed operation guidance system successfully reduced the variance of HMT by 1.9°C.

show abstract

Ensemble Non-Gaussian Local Regression for Industrial Silicon Content Prediction

Cited by 13 publications

References 47 publications

Kinetics of Dissolution of SiO Gas in Liquid Fe–C Alloys

Kinetics of Dissolution of SiO Gas in Liquid Fe–C Alloys

An Operator Behavior Model for Thermal Control of Blast Furnace

Practical Operation Guidance on Thermal Control of Blast Furnace

Contact Info

Product

Resources

About