Controlling Minor Element Phosphorus in Green Electric Steelmaking Using Neural Networks

Elmira Moosavi-Khoonsari; Riadh Azzaz; Valentin Hurel; Mohammad Jahazi; Samira Ebrahimi Kahou

doi:10.1007/978-3-031-80892-0_28

Controlling Minor Element Phosphorus in Green Electric Steelmaking Using Neural Networks

Research output: Contribution to Book/Report types › Contribution to conference proceedings › peer-review

Abstract

The scrap-basedScrap recyclingelectric arc furnaceElectric arc furnace (EAF) is pivotal in sustainable steelmakingSteelmaking by recyclingRecycling steel and minimizing raw materialRaw materials extraction. The precise control of phosphorus, a critical impurity affecting steel quality, remains a significant challenge in the industryIndustry. This work details the development of an advanced artificial neural network (ANNArtificial Neural Network (ANN)) model designed to predict the final phosphorus content of steel based on the process parameters within an EAF. This model leverages systematic data integration and rigorous model validation, demonstrating superior predictive accuracy compared to existing models. Inherent model limitations will also be addressed and future research directions aimed at further enhancing predictive capabilities and expanding the applicability of the proposed approach in steelmakingSteelmaking context will be presented. Industrial implementation of the model will be discussed, highlighting opportunities to optimize EAF operations for improved green steel quality.

Original language	English
Title of host publication	REWAS 2025 - Circular Economy for the Energy Transition
Editors	Adamantia Lazou, Christina Meskers, Elsa Olivetti, Fabian Diaz, Mertol Gökelma
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	297-305
Number of pages	9
ISBN (Print)	9783031808913
DOIs	https://doi.org/10.1007/978-3-031-80892-0_28
Publication status	Published - 2025
Event	8th installment of the REWAS conference series held at the TMS Annual Meeting and Exhibition focuses on circular economy for the energy transition, 2025 - Las Vegas, United States Duration: 23 Mar 2025 → 27 Mar 2025

Publication series

Name	Minerals, Metals and Materials Series
ISSN (Print)	2367-1181
ISSN (Electronic)	2367-1696

Conference

Conference	8th installment of the REWAS conference series held at the TMS Annual Meeting and Exhibition focuses on circular economy for the energy transition, 2025
Country/Territory	United States
City	Las Vegas
Period	23/03/25 → 27/03/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

!!!Keywords

Artificial neural network (ANN)
Dephosphorization
Electric arc furnace
Machine learning
Scrap recycling
Steelmaking
Sustainable steel production

Access to Document

10.1007/978-3-031-80892-0_28

Cite this

Moosavi-Khoonsari, E., Azzaz, R., Hurel, V., Jahazi, M., & Ebrahimi Kahou, S. (2025). Controlling Minor Element Phosphorus in Green Electric Steelmaking Using Neural Networks. In A. Lazou, C. Meskers, E. Olivetti, F. Diaz, & M. Gökelma (Eds.), REWAS 2025 - Circular Economy for the Energy Transition (pp. 297-305). (Minerals, Metals and Materials Series). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-80892-0_28

Moosavi-Khoonsari, Elmira ; Azzaz, Riadh ; Hurel, Valentin et al. / Controlling Minor Element Phosphorus in Green Electric Steelmaking Using Neural Networks. REWAS 2025 - Circular Economy for the Energy Transition. editor / Adamantia Lazou ; Christina Meskers ; Elsa Olivetti ; Fabian Diaz ; Mertol Gökelma. Springer Science and Business Media Deutschland GmbH, 2025. pp. 297-305 (Minerals, Metals and Materials Series).

@inproceedings{f197ceced79b4495842ddfad0ba1d05a,

title = "Controlling Minor Element Phosphorus in Green Electric Steelmaking Using Neural Networks",

abstract = "The scrap-basedScrap recyclingelectric arc furnaceElectric arc furnace (EAF) is pivotal in sustainable steelmakingSteelmaking by recyclingRecycling steel and minimizing raw materialRaw materials extraction. The precise control of phosphorus, a critical impurity affecting steel quality, remains a significant challenge in the industryIndustry. This work details the development of an advanced artificial neural network (ANNArtificial Neural Network (ANN)) model designed to predict the final phosphorus content of steel based on the process parameters within an EAF. This model leverages systematic data integration and rigorous model validation, demonstrating superior predictive accuracy compared to existing models. Inherent model limitations will also be addressed and future research directions aimed at further enhancing predictive capabilities and expanding the applicability of the proposed approach in steelmakingSteelmaking context will be presented. Industrial implementation of the model will be discussed, highlighting opportunities to optimize EAF operations for improved green steel quality.",

keywords = "Artificial neural network (ANN), Dephosphorization, Electric arc furnace, Machine learning, Scrap recycling, Steelmaking, Sustainable steel production",

author = "Elmira Moosavi-Khoonsari and Riadh Azzaz and Valentin Hurel and Mohammad Jahazi and \{Ebrahimi Kahou\}, Samira",

note = "Publisher Copyright: {\textcopyright} The Minerals, Metals \& Materials Society 2025.; 8th installment of the REWAS conference series held at the TMS Annual Meeting and Exhibition focuses on circular economy for the energy transition, 2025 ; Conference date: 23-03-2025 Through 27-03-2025",

year = "2025",

doi = "10.1007/978-3-031-80892-0\_28",

language = "English",

isbn = "9783031808913",

series = "Minerals, Metals and Materials Series",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "297--305",

editor = "Adamantia Lazou and Christina Meskers and Elsa Olivetti and Fabian Diaz and Mertol G{\"o}kelma",

booktitle = "REWAS 2025 - Circular Economy for the Energy Transition",

}

Moosavi-Khoonsari, E, Azzaz, R, Hurel, V, Jahazi, M & Ebrahimi Kahou, S 2025, Controlling Minor Element Phosphorus in Green Electric Steelmaking Using Neural Networks. in A Lazou, C Meskers, E Olivetti, F Diaz & M Gökelma (eds), REWAS 2025 - Circular Economy for the Energy Transition. Minerals, Metals and Materials Series, Springer Science and Business Media Deutschland GmbH, pp. 297-305, 8th installment of the REWAS conference series held at the TMS Annual Meeting and Exhibition focuses on circular economy for the energy transition, 2025, Las Vegas, United States, 23/03/25. https://doi.org/10.1007/978-3-031-80892-0_28

Controlling Minor Element Phosphorus in Green Electric Steelmaking Using Neural Networks. / Moosavi-Khoonsari, Elmira; Azzaz, Riadh; Hurel, Valentin et al.
REWAS 2025 - Circular Economy for the Energy Transition. ed. / Adamantia Lazou; Christina Meskers; Elsa Olivetti; Fabian Diaz; Mertol Gökelma. Springer Science and Business Media Deutschland GmbH, 2025. p. 297-305 (Minerals, Metals and Materials Series).

Research output: Contribution to Book/Report types › Contribution to conference proceedings › peer-review

TY - GEN

T1 - Controlling Minor Element Phosphorus in Green Electric Steelmaking Using Neural Networks

AU - Moosavi-Khoonsari, Elmira

AU - Azzaz, Riadh

AU - Hurel, Valentin

AU - Jahazi, Mohammad

AU - Ebrahimi Kahou, Samira

N1 - Publisher Copyright: © The Minerals, Metals & Materials Society 2025.

PY - 2025

Y1 - 2025

N2 - The scrap-basedScrap recyclingelectric arc furnaceElectric arc furnace (EAF) is pivotal in sustainable steelmakingSteelmaking by recyclingRecycling steel and minimizing raw materialRaw materials extraction. The precise control of phosphorus, a critical impurity affecting steel quality, remains a significant challenge in the industryIndustry. This work details the development of an advanced artificial neural network (ANNArtificial Neural Network (ANN)) model designed to predict the final phosphorus content of steel based on the process parameters within an EAF. This model leverages systematic data integration and rigorous model validation, demonstrating superior predictive accuracy compared to existing models. Inherent model limitations will also be addressed and future research directions aimed at further enhancing predictive capabilities and expanding the applicability of the proposed approach in steelmakingSteelmaking context will be presented. Industrial implementation of the model will be discussed, highlighting opportunities to optimize EAF operations for improved green steel quality.

AB - The scrap-basedScrap recyclingelectric arc furnaceElectric arc furnace (EAF) is pivotal in sustainable steelmakingSteelmaking by recyclingRecycling steel and minimizing raw materialRaw materials extraction. The precise control of phosphorus, a critical impurity affecting steel quality, remains a significant challenge in the industryIndustry. This work details the development of an advanced artificial neural network (ANNArtificial Neural Network (ANN)) model designed to predict the final phosphorus content of steel based on the process parameters within an EAF. This model leverages systematic data integration and rigorous model validation, demonstrating superior predictive accuracy compared to existing models. Inherent model limitations will also be addressed and future research directions aimed at further enhancing predictive capabilities and expanding the applicability of the proposed approach in steelmakingSteelmaking context will be presented. Industrial implementation of the model will be discussed, highlighting opportunities to optimize EAF operations for improved green steel quality.

KW - Artificial neural network (ANN)

KW - Dephosphorization

KW - Electric arc furnace

KW - Machine learning

KW - Scrap recycling

KW - Steelmaking

KW - Sustainable steel production

UR - https://www.scopus.com/pages/publications/86000014109

U2 - 10.1007/978-3-031-80892-0_28

DO - 10.1007/978-3-031-80892-0_28

M3 - Contribution to conference proceedings

AN - SCOPUS:86000014109

SN - 9783031808913

T3 - Minerals, Metals and Materials Series

SP - 297

EP - 305

BT - REWAS 2025 - Circular Economy for the Energy Transition

A2 - Lazou, Adamantia

A2 - Meskers, Christina

A2 - Olivetti, Elsa

A2 - Diaz, Fabian

A2 - Gökelma, Mertol

PB - Springer Science and Business Media Deutschland GmbH

T2 - 8th installment of the REWAS conference series held at the TMS Annual Meeting and Exhibition focuses on circular economy for the energy transition, 2025

Y2 - 23 March 2025 through 27 March 2025

ER -

Moosavi-Khoonsari E, Azzaz R, Hurel V, Jahazi M , Ebrahimi Kahou S. Controlling Minor Element Phosphorus in Green Electric Steelmaking Using Neural Networks. In Lazou A, Meskers C, Olivetti E, Diaz F, Gökelma M, editors, REWAS 2025 - Circular Economy for the Energy Transition. Springer Science and Business Media Deutschland GmbH. 2025. p. 297-305. (Minerals, Metals and Materials Series). doi: 10.1007/978-3-031-80892-0_28

Controlling Minor Element Phosphorus in Green Electric Steelmaking Using Neural Networks

Abstract

Publication series

Conference

UN SDGs

!!!Keywords

Access to Document

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