A dynamic, first-principles process model for a steelmaking electric arc furnace has been developed. The model is an integrated part of an application designed for optimization during operation of the furnace. Special care has been taken to ensure that the non-linear model is robust and accurate enough for real-time optimization. The model is formulated in terms of state variables and ordinary differential equations and is adapted to process data using recursive parameter estimation. Compared to other models available in the literature, a focus of this model is to integrate auxiliary process data in order to best predict energy efficiency and heat transfer limitations in the furnace. Model predictions are in reasonable agreement with steel temperature and weight measurements. Simulations indicate that industrial deployment of Model Predictive Control applications derived from this process model can result in electrical energy consumption savings of 1–2%.

A laser vibrometer system in combination with appropriate artificial intelligence methods for clustering of the measured vibration spectra was tested at a continuous steel casting machine to receive information on the solidification status of the strand. Measurements with the laser vibrometer at a fixed strand position of the billet caster of ESF under conditions of incrementally increasing casting speeds revealed a transition in the population of the identified vibration clusters as a footprint of the passed crater end position with a change from a fully solidified strand to a strand with some liquid core at the measurement position. This was in accordance with the results from a three-dimensional dynamic temperature and solidification model which was set up based on a state-of-the-art approach for solution of the heat flow equation with tailored submodels for the different boundary zones of the ESF billet caster (i.e., mould, secondary spray water zones and radiation zones) and installed at the steel plant for online monitoring and control of the casting process. The application of the newly installed measurement and model-based information systems at ESF revealed significant improvements in their billet casting process in terms of halved strand breakout rates and correspondingly increased productivity.

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%.