Citation: Hosseinzadeh, M.;
Mashhadimoslem, H.; Maleki, F.;
Elkamel, A. Prediction of Solid
Conversion Process in Direct
Reduction Iron Oxide Using Machine
Learning. Energies 2022, 15, 9276.
https://doi.org/10.3390/
en15249276
Academic Editors:
Luis Hernández-Callejo,
Sergio Nesmachnow and
Sara Gallardo Saavedra
Received: 7 November 2022
Accepted: 4 December 2022
Published: 7 December 2022
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Article
Prediction of Solid Conversion Process in Direct Reduction Iron
Oxide Using Machine Learning
Masih Hosseinzadeh
1
, Hossein Mashhadimoslem
1,2
, Farid Maleki
3
and Ali Elkamel
2,4,
*
1
Department of Chemical Engineering, Iran University of Science and Technology (IUST), Narmak,
Tehran 16846, Iran
2
Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
3
Department of Polymer Engineering & Color Technology, Amirkabir University of Technology,
Tehran 15916, Iran
4
Department of Chemical Engineering, Khalifa University, Abu Dhabi P.O. Box. 59911, United Arab Emirates
* Correspondence: aelkamel@uwaterloo.ca
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
1. Introduction
Direct reduction of iron oxide (DRI) is one of the most important non-catalytic gas-
solid reactions in industry, and it continues to be an important field of study in chemical
engineering [
1
,
2
]. The MIDREX process, which is one of the direct-reduction technologies,
has received a lot of interest because it is a great technology for considerably reducing
carbon dioxide (CO
2
) emissions from steel plants [
3
,
4
]. This is primarily accomplished by
using natural gas instead of coke or coal [
5
]. Several approaches were used to develop
these solutions, whose overview is provided in Figure 1.
Energies 2022, 15, x FOR PEER REVIEW 2 of 29
Figure 1. Direct reduction methods have been developed extensively [6].
Despite the global COVID-19 pandemic, global DRI output in 2020 is 104.4 million
tonnes which has a 3.4% decrease compared with the previous year’s record of 108.1 mil-
lion tonnes. India and Iran produced about half of the world’s DRI [7].
The shaft furnace, reformer, and recuperator are the three main parts of the Midrex
process, of which the shaft furnace is the most important. Within the shaft furnace, reduc-
tion processes take place, and iron oxide turns into sponge iron. Researchers have recently
worked to regenerate hydrogen and develop the new MIDREX process design. Pimm et
al. improved the MIDREX process to use renewable energies to satisfy the energy needs
of the revised MIDREX process and the hydrogen-based MIDREX unit. According to
Rechberger et al.’s research, the carbon footprint of the power used to manufacture hy-
drogen has a significant impact on the amount of potential that the hydrogen-based path-
way offers for environmentally friendly steelmaking [8,9].
Figure 2 indicates direct reduction processes for the production of sponge iron which
uses natural gas as the major reducing agent. Today these processes provide for more than
70% of the overall production of DRI and hot briquetted iron (HBI). Natural gas is trans-
formed into reducing agents, mostly carbon monoxide and hydrogen, which operate as
iron oxide reducers [6]. The shaft furnace is divided into three main parts: (i) reduction
zone, (ii) transition zone, and (iii) cooling zone. The most fundamental part of the shaft
furnace is the place where reduction occurs. Therefore, most of the modeling has been
conducted around this area. The unreacted shrinking core model (USCM) is an assump-
tion adopted by the majority of prior simulations at the pellet scale [10–12]. Furthermore,
some modeled direct reduction reactors in industrial units use this model and achieved
desirable results [13,14]. Nevertheless, the grain model can be better than the USCM at
predicting plant data [15].
Figure 1. Direct reduction methods have been developed extensively [6].
Energies 2022, 15, 9276. https://doi.org/10.3390/en15249276 https://www.mdpi.com/journal/energies
