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Optimizing electricity consumption in direct reduction iron processes using RSM, MLP, and RBF models.

Erfan Gholamzadeh1, Ahad Ghaemi2

  • 1School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran , Iran.

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|October 7, 2025
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Optimizing direct reduction iron (DRI) units through machine learning models like MLP significantly cuts energy use. This research identifies key operational adjustments for substantial energy savings and improved efficiency in steel production.

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Area of Science:

  • Metallurgical Engineering
  • Data Science
  • Energy Management

Background:

  • Direct reduction iron (DRI) units are crucial in steel manufacturing but face significant energy consumption challenges.
  • Optimizing energy efficiency in DRI processes is vital for economic viability and environmental sustainability.

Purpose of the Study:

  • To identify key factors influencing energy consumption in DRI units.
  • To develop and compare advanced machine learning models for predicting and optimizing energy usage.
  • To determine optimal operational parameters for minimizing energy consumption in DRI processes.

Main Methods:

  • Collected and analyzed operational data from a DRI unit.
  • Employed Response Surface Methodology (RSM), Multilayer Perceptron (MLP), and Radial Basis Function (RBF) neural networks for modeling.
  • Evaluated model performance using coefficient of determination (R²) and Mean Squared Error (MSE).

Main Results:

  • ANN models, particularly MLP (R²=0.99601), outperformed RSM (R²=0.9879) in accuracy.
  • Optimized MLP model identified key parameters like cooling gas flow and main burner flow for energy reduction.
  • Predicted daily savings of 60,000 kWh and annual savings of ~21,900,000 kWh (10.34% efficiency improvement).

Conclusions:

  • Machine learning, specifically MLP, offers a powerful tool for optimizing energy consumption in DRI units.
  • Data-driven energy management strategies can lead to significant cost savings and enhanced sustainability in energy-intensive industries.
  • Strategic adjustments to operational parameters are effective in achieving substantial energy efficiency gains in steel production.