Application of an Artificial Neural Network for Efficient Computation of Chemical Activities within an EAF Process Model

The electric arc furnace (EAF) is considered the second most important process for the production of crude steel and is usually used for the melting of scrap. With the current emphasis on defossilization, its share in global steelmaking is likely to further increase. Due to the large production quantities, minor improvements to the EAF process can still accumulate into a significant reduction in overall energy and resource consumption. A major aspect in the efficient operation of the EAF is achieving beneficial slag properties, as the slag influences the composition of the steel and can reduce energy losses as well as the maintenance cost. In order to investigate the EAF operation, a dynamic process model is applied. Within the model, the chemical reactions of the metal–slag system are calculated based on the activities of the involved species. In this regard, multiple models for the calculation of the chemical activities have been implemented. However, depending on the chosen model, the computation of the slag activities can be computationally demanding. For this reason, the application of a neural network for the calculation of the chemical activities within the slag is investigated. The performance of the neural network is then compared to the results of the previously applied models by using the commercial software FactSage as a reference. The validation shows that the surrogate model achieves great accuracy while keeping the computation demand low.