Abstract
This article proposes a system for monitoring a blast furnace operation based on DB-Mini-KMeans and multi-layer perceptron (MLP), which solves a difficult problem categorising the furnace operation types. The system uses the correlation between cooling wall temperature and furnace operation type, creates an furnace operation type recognition model with optimised K-Means and MLP algorithms and makes a comparison test with other recognition models; evaluates all kinds of operation types by combining six blast furnace production indexes, such as gas utilisation rate and airflow and establishes a monitoring system of furnace operation types in blast furnaces. The clustering performance of the system has been improved by nearly 40%, and the recognition accuracy of various operation types has reached more than 90%, covering functions such as two-dimensional schematic diagrams of furnace operation types and status warnings, which provide help and support for people to analyse furnace conditions.
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 callMore From: Ironmaking & Steelmaking: Processes, Products and Applications
Disclaimer: 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.
