Sticking Behavior of Pellets During Direct Reduction Based on Hydrogen Metallurgy: An Optimization Approach Using Response Surface Methodology

Feng, Jinge; Tang, Jue; Chu, Mansheng; Liu, Peijun; Zhao, Zichuan; Zheng, Aijun; Wang, Xiaoai; Han, Tao

doi:10.1007/s40831-023-00712-2

J. Sustain. Metall.

2023

DOI: 10.1007/s40831-023-00712-2

|View full text |Cite

Sticking Behavior of Pellets During Direct Reduction Based on Hydrogen Metallurgy: An Optimization Approach Using Response Surface Methodology

Jinge Feng

Jue Tang

Mansheng Chu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Modeling of Gaseous Reduction of Iron Oxide Pellets Using Machine Learning Algorithms, Explainable Artificial Intelligence, and Hyperparameter Optimization Techniques

Hosseinzadeh,

Kasiri,

Rezaei

2024

steel research int.

View full text Add to dashboard Cite

In this study, a novel application of machine learning (ML) is introduced to pellet modeling in the intricate non‐catalytic gas–solid reaction of direct reduction of iron oxide in the steel industry. Twenty ML models are developed using four algorithms: multilayer perceptron neural networks (MLPNN), radial basis function neural network (RBFNN), support vector regression, and random forest (RF). Hyperparameter optimization is conducted using Bayesian algorithms, random search, and grid search. The optimum model achieves a mean squared error test of 0.0052 with random RF for the larger dataset (872 samples), while smaller datasets (132, 225, and 242 samples) produce optimum models with MLPNN and RBFNN. Hyperparameters vary between the larger datasets and the smaller datasets. The models offer insight into the complex interactions among variables, including time, temperature, gas composition, hematite composition, pellet radius, and initial pellet porosity, influencing the metallization degree. In this study, the significant role of time and temperature is emphasized, as revealed by explainable artificial intelligence using Shapley additive explanation analysis that utilizes the game theory, and the effects of pellet modeling parameters are elucidated through 3D plots, particularly highlighting the impact of changing H2/CO proportion on metallization degree and carbon deposit.

show abstract

Modeling of Gaseous Reduction of Iron Oxide Pellets Using Machine Learning Algorithms, Explainable Artificial Intelligence, and Hyperparameter Optimization Techniques

Hosseinzadeh,

Kasiri,

Rezaei

2024

steel research int.

View full text Add to dashboard Cite

show abstract

Effect of V2O5 During Oxidation Roasting of Pellet: Non-isothermal Kinetic Analysis and Microstructure Evolution

Feng,

Tang,

Wang

et al. 2024

J. Sustain. Metall.

View full text Add to dashboard Cite

Effects of Metallization Degree of DRI on the Yield and CO2 Emission in Reduction Shaft Furnace Process

Zhou,

Jiang,

Wang

et al. 2024

J. Sustain. Metall.

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Sticking Behavior of Pellets During Direct Reduction Based on Hydrogen Metallurgy: An Optimization Approach Using Response Surface Methodology

Cited by 3 publications

References 47 publications

Modeling of Gaseous Reduction of Iron Oxide Pellets Using Machine Learning Algorithms, Explainable Artificial Intelligence, and Hyperparameter Optimization Techniques

Modeling of Gaseous Reduction of Iron Oxide Pellets Using Machine Learning Algorithms, Explainable Artificial Intelligence, and Hyperparameter Optimization Techniques

Effect of V2O5 During Oxidation Roasting of Pellet: Non-isothermal Kinetic Analysis and Microstructure Evolution

Effects of Metallization Degree of DRI on the Yield and CO2 Emission in Reduction Shaft Furnace Process

Contact Info

Product

Resources

About