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

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%.

Prediction of Converter Tapping Weight Based on Principal Component Analysis–Whale Optimization Algorithm–Backpropagation Algorithm Neural Network Model

“…To eliminate the influence of dimensional differences and singular samples, the data were standardized and the original data were normalized. The normalization process formula is shown in Equation (12).…”

“…[8,11] Thus, intelligent algorithms, such as machine learning and artificial neural networks, can be widely used in the data processing and parameter prediction of complex industrial systems. [12][13][14][15] The predicted values of the unknown parameters are obtained by inputting real-time data into a trained artificial neural network. One of the most widely used models is the feedforward neural network model trained using the backpropagation algorithm (BP).…”

A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework

“…The actor-critic approach is characterised by its robustness [172] and acts as an upgrade of the traditional Q-learning, which could act as a decision-support system easing operators scheduling tasks [173,174]. Through the actor-critic approach, the policy is periodically checked and recalibrated to the situation, which highly increases the adaptability and eases the implementation in real-time scheduling [96,175].…”