Multiscale modeling of non-catalytic gas-solid reactions applied to the hydrogen direct reduction of iron ore in moving-bed reactor

Metolina, Patrícia; Nunis da Silva, André Luiz; Dixon, Anthony G.; Guardani, Roberto

doi:10.1016/j.ijhydene.2024.03.151

International Journal of Hydrogen Energy

2024

DOI: 10.1016/j.ijhydene.2024.03.151

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Multiscale modeling of non-catalytic gas-solid reactions applied to the hydrogen direct reduction of iron ore in moving-bed reactor

Patrícia Metolina,

André Luiz Nunis da Silva,

Anthony G. Dixon

et al.

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Cited by 7 publications

References 42 publications

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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.

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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.

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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

Multi-dimensional CFD-Mask R-CNN and CFD-watershed segmentation approach for multiphase non-catalytic gas-solid reactions: A case study for hydrogen reduction of porous iron oxide pellets

Hosseinzadeh,

Kasiri,

Rezaei

et al. 2024

Chemical Engineering Journal

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Assessing the potential of low-temperature ironmaking using pure hydrogen in shaft reactors

Skaf,

Tiruta-Barna,

Ahmadi

2025

Chemical Engineering Science

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Multiscale modeling of non-catalytic gas-solid reactions applied to the hydrogen direct reduction of iron ore in moving-bed reactor

Cited by 7 publications

References 42 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

Multi-dimensional CFD-Mask R-CNN and CFD-watershed segmentation approach for multiphase non-catalytic gas-solid reactions: A case study for hydrogen reduction of porous iron oxide pellets

Assessing the potential of low-temperature ironmaking using pure hydrogen in shaft reactors

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