Increasing market demands on quality of the steel, steel price and production times are leading to introduction of many technological innovations regarding the electric arc furnaces (EAFs). One of the areas with significant potential is also advanced computer support of the EAF process, which allows data acquisition, advanced monitoring and proper control of the EAF. In the most advanced form of such system, its basis can be represented by mathematical process models, capable of online estimation of the crucial process values, which are otherwise not measured, such as chemical compositions and temperatures of the steel, slag and gas.
In this paper, idea and development of all key EAF-process models (electrical, thermal, mass-transfer and chemical), which are used for estimation of the unmeasured values, are presented.
The validation results that were performed using operational EAF measurements indicate high levels of estimation accuracy, which allows the usage of these models in broader environments, for either soft sensing and monitoring or process optimization and decision support.