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

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%.

Automated summary

This study aims to implement an operator support system that improves the efficiency of the oxygen blowing process in a real cast steel foundry by developing a machine learning agent using reinforcement learning method. The trained agent, integrated into the factory, continues to improve with real experience, leading to a 12% increase in the success rate of the process.