Abstract
Due to the importance of the steel industry in the national economy and the inherent complexity of operating a blast furnace, it is necessary to study ways to optimize its operation and the consumption of resources, therefore, this work aims to investigate the use of the LSTM (Long Short Term Memory) neural network to perform the prediction of the next temperature of the hot metal being produced. In this way, it is possible to support the work of the blast furnace operators, in order to optimize the consumption of resources to keep the blast furnace operating. With the results obtained from the experiments using the blast furnace operating data as a time series, it is concluded that the use of LSTM is satisfactory and that improvement of these experiments will meet the needs of the steel industry. The best result for LSTM, using 2 layers and 2048 neurons, achieved a Root Mean Square Error of 11.82ºC.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
