Citation: Ojeda Roldán, Á.; Gassner,
G.; Schlautmann, M.; Acevedo
Galicia, L.E.; Andreiana, D.S.;
Heiskanen, M.; Leyva Guerrero, C.;
Dorado Navas, F.; del Real Torres, A.
Optimisation of Operator Support
Systems through Artificial
Intelligence for the Cast Steel
Industry: A Case for Optimisation of
the Oxygen Blowing Process Based
on Machine Learning Algorithms. J.
Manuf. Mater. Process. 2022, 6, 34.
https://doi.org/10.3390/
jmmp6020034
Academic Editors: Kelvin K.L. Wong,
Dhanjoo N. Ghista, Andrew W.H. Ip
and Wenjun (Chris) Zhang
Received: 2 February 2022
Accepted: 9 March 2022
Published: 12 March 2022
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Manufacturing and
Materials Processing
Journal of
Article
Optimisation of Operator Support Systems through Artificial
Intelligence for the Cast Steel Industry: A Case for
Optimisation of the Oxygen Blowing Process Based on Machine
Learning Algorithms
Álvaro Ojeda Roldán
1,
* , Gert Gassner
2
, Martin Schlautmann
3
, Luis Enrique Acevedo Galicia
1
,
Doru Stefan Andreiana
1
, Mikko Heiskanen
4
, Carlos Leyva Guerrero
1
, Fernando Dorado Navas
1
and Alejandro del Real Torres
1
1
Idener, IT Department, 41300 Sevilla, Spain; luisenrique.acevedo@idener.es (L.E.A.G.);
doru.stefan@idener.es (D.S.A.); carlos.leyva@idener.es (C.L.G.); fernando.dorado@idener.es (F.D.N.);
alejandro.delreal@idener.es (A.d.R.T.)
2
Maschinenfabrik Liezen und Gießerei Ges.m.b.H. (MFL), 8940 Liezen, Austria; g.gassner@mfl.at
3
VDEh-Betriebsforschungsinstitut GmbH (BFI), 40237 Düsseldorf, Germany; martin.schlautmann@bfi.de
4
VTT—Technical Research Centre of Finland, 2044 Espoo, Finland; mikko.heiskanen@vtt.fi
* Correspondence: alvaro.ojeda@idener.es
Abstract:
The processes involved in the metallurgical industry consume significant amounts of energy
and materials, so improving their control would result in considerable improvements in the efficient
use of these resources. This study is part of the MORSE H2020 Project, and it aims to implement an
operator support system that improves the efficiency of the oxygen blowing process of a real cast
steel foundry. For this purpose, a machine learning agent is developed according to a reinforcement
learning method suitable for the dynamics of the oxygen blowing process in the cast steel factory.
This reinforcement learning agent is trained with both historical data provided by the company and
data generated by an external model. The trained agent will be the basis of the operator support
system that will be integrated into the factory, allowing the agent to continue improving with new
and real experience. The results show that the suggestions of the agent improve as it gains experience,
and consequently the efficiency of the process also improves. As a result, the success rate of the
process increases by 12%.
Keywords:
oxygen blowing process; cast steel; machine learning; artificial intelligence; reinforcement
learning; Q-learning; training
1. Introduction
The fourth industrial revolution, also called Industry 4.0, has increased the digitisation
and automation of many industrial sectors through the development of smart factories [
1
].
The objective is to enhance production through processes optimisation, environmental
protection and data management, among other factors [
2
]. To this end, the integration of
AI-based techniques is playing an important role, highlighting a machine learning (ML)
paradigm called reinforcement learning (RL). Thanks to its capability for learning from
interaction, this ML paradigm has become a clear alternative to classic control methods to
control industrial processes [
3
]. In fact, in many cases, RL control methods enhance process
efficiencies [
4
,
5
], such as welding with robot manipulators [
6
] and controlling cooling water
systems [7] and chemical processes [8].
The steel industry has a broad scope for efficiency improvements due to its high rates
of energy and material consumption. Moreover, most techniques used in the steelmaking
processes are based on the know-how and professional experience of experts in the sector [
9
].
Focusing on cast steel foundries with an electric arc furnace (EAF) in their production
J. Manuf. Mater. Process. 2022, 6, 34. https://doi.org/10.3390/jmmp6020034 https://www.mdpi.com/journal/jmmp
