metafur: Digital Twin System of a Blast Furnace

Abstract

Blast furnace ironmaking accounts for approximately 70% of the total energy consumption and emissions in steelmaking. Hot metal quality has a significant impact on the operation of steelmaking units while blast furnace productivity and fuel rate impact economics of the entire steel plant. We have developed a digital twin system ‘metafur’ for an integrated blast furnace that receives burden quality and process data from various sources in real-time, predicts blast furnace KPIs, and identifies and recommends setpoints for manipulated variables to optimize the KPIs for any given burden quality. The digital twin system is designed to address day-to-day operational challenges of the blast furnace by interacting with an actual blast furnace in real-time. It comprises communication, real-time data pre-processing, time lag and regime identification, blast furnace models, online blast furnace optimizer, and self-monitoring and self-learning modules. It has been tested with data from multiple industrial-scale blast furnaces. Process optimization using metafur revealed opportunities for improving productivity and fuel consumption at multiple agglomerate levels. metafur would be a useful tool for real-time monitoring, optimization, and sustainable operation of industrial blast furnaces.