A Hybrid Digital Twin for Optimal Si-Production

Abstract

Production of high silicon alloys must adhere to a range of expectations and demands. In addition to meeting the customer expectations on quality, the production process itself should be operated such that the raw material and energy consumption is minimized per metric ton produced. Elkem is working on upgrading the decision support systems for plant operators to meet the above-mentioned expectations. In this work, new sensor technology coupled with data-based models has been applied to develop an online decision support system (aka hybrid twin) for production of FeSi75. Infrared camera technology has been applied to the tapping, refining/alloying and casting processes and some preliminary results will be presented, focusing on determining the amount of slag produced in the furnace and tapped with the liquid alloy. In parallell, a data-driven model has been applied to furnace operational parameters in order to predict the amount of slag Based on initial results, application of IR technology combined with image analysis seems to be a viable path forward in order to establish more precise decision support models that will improve product quality and increase yield.

Keywords: Hybrid twin, mathematical model, real-time system, control, optimization, monitoring