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

Masih Hosseinzadeh, Hossein Mashhadimoslem, Farid Maleki and Ali Elkamel ()
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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
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

Energies, 2022, vol. 15, issue 24, 1-25

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.

Keywords: direct reduction; MIDREX; neural network; optimization; algorithm; modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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