Work

Machine Learning-Based Time-Series Forecasting for Off-Gas Characterization and Decarbonization in Electric Arc Furnace Steelmaking

Year: 2026

Type: article

Abstract: High Resolution Image Download MS PowerPoint Slide Electric arc furnace (EAF) steel production substantially reduces CO 2 emissions from steelmaking but is not fully carbon neutral. To facilitate full decarbonization of this batch process, machine learning-based time-series forecasting models are constructed to predict the EAF off-gas composition (CO 2, CO and H 2 O) on a second-by-second basis. These models will be used to drive the design and testing of carbon capture technologies that can handle dynamically changing flue gases. The resultant models successfully forecasted the behavior of the off-gas composition, including the stochastic and erratic behavior of the variables, for both a scrap-fed EAF and a DRI-fed EAF. Generation of numerous predictions for the same future heat creates a distribution of the most likely operating conditions at each point in time. This ensures that carbon capture equipment for downstream decarbonization can be appropriately designed to handle the full breadth of the operational variability of the system.

Source: Industrial & Engineering Chemistry Research

DOI: 10.1021/acs.iecr.5c02325

Authors: Nicole K. Bond, Farzad Hourfar, Robin W. Hughes, Laura Chin, Ali Elkamel, Biao Huang