Abstract
In the process of LF furnace steelmaking, alloys need to be added in the tapping process for deoxidation and composition fine-tuning. Aiming at the problem of alloy addition, an alloy charging optimization model is designed. First, use the radial basis function (RBF) neural network to establish the alloy yield prediction model; then use the improved particle swarm (PSO) algorithm to optimize the radial basis function neural network, and obtain the optimal neural network weights and thresholds through training; finally use the actual production data is simulated, and the results show that the model effectively improves the prediction accuracy of alloy yield.
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.
