|About this Abstract
||2018 TMS Annual Meeting & Exhibition
||Materials Processing Fundamentals
||Hybrid Modeling for Endpoint Carbon Content Prediction in EAF Steelmaking
||Wei Guangsheng, Zhu Rong, Yang Lingzhi, Tang Tianping
|On-Site Speaker (Planned)
Considering the complicated and harsh conditions in the EAF steelmaking process, the precise endpoint control technology is a crux that hinders the development of the short steelmaking process. In this paper, a new hybrid prediction model was established to predict the endpoint carbon content in EAF steelmaking, which included the mechanism model based on the mass transfer process and the ELM algorithm optimized by the EMA algorithm. The mechanism model was calibrated with corrected parameters obtained from the intelligent algorithm. As a result, the shortages that the mechanism model can’t work precisely and the single intelligent algorithm model lacks the analysis of the metallurgy process were overcome by the hybrid prediction model. Meanwhile, modifying ELM algorithm by EMA algorithm can improve the generalization performance of single-hidden-layer feed-forward neural networks. The experiments on a 50t EAF demonstrated that the proposed model had a good generalization performance and good prediction accuracy.
||Planned: None Selected