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Prediction of Solid Conversion Process in Direct Reduction Iron Oxide Using Machine Learning

Author

Listed:
  • Masih Hosseinzadeh

    (Department of Chemical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846, Iran)

  • Hossein Mashhadimoslem

    (Department of Chemical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846, Iran
    Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Farid Maleki

    (Department of Polymer Engineering & Color Technology, Amirkabir University of Technology, Tehran 15916, Iran)

  • Ali Elkamel

    (Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    Department of Chemical Engineering, Khalifa University, Abu Dhabi P.O. Box. 59911, United Arab Emirates)

Abstract

The direct reduction process has been developed and investigated in recent years due to less pollution than other methods. In this work, the first direct reduction iron oxide (DRI) modeling has been developed using artificial neural networks (ANN) algorithms such as the multilayer perceptron (MLP) and radial basis function (RBF) models. A DRI operation takes place inside the shaft furnace. A shaft furnace reactor is a gas-solid reactor that transforms iron oxide particles into sponge iron. Because of its low environmental pollution, the MIDREX process, one of the DRI procedures, has received much attention in recent years. The main purpose of the shaft furnace is to achieve the desired percentage of solid conversion output from the furnace. The network parameters were optimized, and an algorithm was developed to achieve an optimum NN model. The results showed that the MLP network has a minimum squared error (MSE) of 8.95 × 10 −6 , which is the lowest error compared to the RBF network model. The purpose of the study was to identify the shaft furnace solid conversion using machine learning methods without solving nonlinear equations. Another advantage of this research is that the running speed is 3.5 times the speed of mathematical modeling.

Suggested Citation

  • Masih Hosseinzadeh & Hossein Mashhadimoslem & Farid Maleki & Ali Elkamel, 2022. "Prediction of Solid Conversion Process in Direct Reduction Iron Oxide Using Machine Learning," Energies, MDPI, vol. 15(24), pages 1-25, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9276-:d:996219
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    References listed on IDEAS

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    1. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    2. Leo Egghe & Loet Leydesdorff, 2009. "The relation between Pearson's correlation coefficient r and Salton's cosine measure," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(5), pages 1027-1036, May.
    3. Salemi, Sina & Torabi, Morteza & Haghparast, Arash Kashani, 2022. "Technoeconomical investigation of energy harvesting from MIDREX® process waste heat using Kalina cycle in direct reduction iron process," Energy, Elsevier, vol. 239(PE).
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