Academic project
PRONEM: Improving Gas-Use Energy Efficiency in Steelmaking through Neuroevolution
Investigation of neuroevolution and deep neural networks to forecast gas generation and consumption in steelmaking processes.
PRONEM investigated deep neural network architectures and neuroevolution techniques for forecasting variables associated with gas generation and consumption in steelmaking processes, using multivariate time series.
The project proposal describes two related problems: predicting gas generation in steel converters and forecasting gas consumption in subsequent industrial stages. The investigated models aim to support decisions related to energy efficiency in the steel industry.
The Lattes record lists an execution period of 2022–2026 and marks the project as completed. The team recorded there includes Jefferson Oliveira Andrade, Karin Satie Komati (project lead), Leandro Colombi Resendo, Kelly Assis de Souza Gazolli, and other academic and industrial collaborators.