4.6 Article

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

Journal

PROCESSES
Volume 10, Issue 3, Pages -

Publisher

MDPI
DOI: 10.3390/pr10030434

Keywords

machine learning; reinforcement learning; Q-learning; steelmaking process CAS-OB; decision-support system; optimisation algorithm

Funding

  1. EuropeanUnion's Horizon 2020 research and innovation program [768652]
  2. H2020 Societal Challenges Programme [768652] Funding Source: H2020 Societal Challenges Programme

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This paper presents the application of a reinforcement learning algorithm as the core of a decision support system for a steelmaking subprocess. The system aims to assist operators in making proper decisions, improve energy and material efficiency, and reduce environmental impact. By learning from historical data and setting rewards, the system works collaboratively with operators and provides better suggestions.
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.

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