Steelmaking Process Optimised through a Decision Support System Aided by Self-Learning Machine Learning

Doru Stefan Andreiana,Carlos Leyva Guerrero,Seppo Ollila,Luis Enrique Acevedo Galicia,Alejandro Del Real Torres,Fernando Dorado Navas,Álvaro Ojeda Roldán

doi:10.3390/pr10030434

Doru Stefan Andreiana, Carlos Leyva Guerrero + Show 5 more

Open Access

https://doi.org/10.3390/pr10030434

Copy DOI

Journal: Processes	Publication Date: Feb 22, 2022
Citations: 4	License type: CC BY 4.0

Affiliation: Idener (Spain), Svenskt Stål (Finland)

Abstract

This paper presents the application of a reinforcement learning (RL) algorithm, concretely Q-Learning, as the core of a decision support system (DSS) for a steelmaking subprocess, the Composition Adjustment by Sealed Argon-bubbling with Oxygen Blowing (CAS-OB) from the SSAB Raahe steel plant. Since many CAS-OB actions are selected based on operator experience, this research aims to develop a DSS to assist the operator in taking the proper decisions during the process, especially less experienced operators. The DSS is intended to supports the operators in real-time during the process to facilitate their work and optimise the process, improving material and energy efficiency, thus increasing the operation’s sustainability. The objective is that the algorithm learns the process based only on raw data from the CAS-OB historical database, and on rewards set according to the objectives. Finally, the DSS was tested and validated by a developer engineer from the CAS-OB steelmaking plant. The results show that the algorithm successfully learns the process, recommending the same actions as those taken by the operator 69.23% of the time. The algorithm also suggests a better option in 30.76% of the remaining cases. Thanks to the DSS, the heat rejection due to wrong composition is reduced by 4%, and temperature accuracy is increased to 83.33%. These improvements resulted in an estimated reduction of 2% in CO2 emissions, 0.5% in energy consumption and 1.5% in costs. Additionally, actions taken based on the operator’s experience are incorporated into the DSS knowledge, facilitating the integration of operators with lower experience in the process.

Highlights

The Composition Adjustment by Sealed Argon-bubbling with Oxygen Blowing (CAS–OB) is a secondary steelmaking process developed in the 1980s by Nippon Steel Corporation [1]
The been performed performed during two months at the CAS-OB plant
It has been proved that a decision support system (DSS) using the Q-Learning algorithm can learn a complex process such as the steelmaking process CAS-OB

Summary

Introduction

OB) is a secondary steelmaking process developed in the 1980s by Nippon Steel Corporation [1]. The main goals during this process are homogenisation, temperature control and composition adjustment [2]. CAS-OB has become one of the relevant buffer stations in the secondary metallurgy of steelmaking thanks to its capability of good chemical composition control, steel homogeneity, and reheating [3]. The process enables the consistent correction of high alloy composition and the reheating of the steel using the exothermic reaction between oxygen and aluminium. The case under investigation pertains to the SSAB Europe Oy. Learning; Springer: Cham, Switzerland, 2017. C.; Brockman, G.; Chan, B.; Cheung, V.; Debiak, P.; Dennison, C.; Farhi, D.; Fischer, Q.; Hashme, S.; Hesse, C.; et al Dota 2 with large scale deep reinforcement learning.

Objectives

Results

Conclusion