Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
End-point Prediction of BOF Steelmaking Based on KNNWTSVR and LWOA
Ist Teil von
Transactions of the Indian Institute of Metals, 2019-01, Vol.72 (1), p.257-270
Ort / Verlag
New Delhi: Springer India
Erscheinungsjahr
2019
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
Basic oxygen furnace (BOF) steelmaking plays an important role in steelmaking process. Therefore, research on BOF steelmaking modeling is very necessary. In this paper, a novel combination prediction model has been proposed, which consists of a time series prediction model and a compensation prediction model. Both models are established by
k
-nearest neighbor-based weighted twin support vector regression (KNNWTSVR) algorithm. By introducing Lévy flight algorithm and inertia weight, an improved algorithm of whale optimization algorithm (WOA) called Lévy flight WOA has been initially proposed to solve the optimization problem in the objective function of KNNWTSVR. The simulation results show that the proposed models are effective and feasible. Within different error bounds (0.005% for carbon content model and 10 °C for temperature model), the strike rates of carbon content and temperature both achieve 93%, and a double strike rate of 86% is obtained, which can provide a significant reference for real BOF applications, and the proposed method is also appropriate for the prediction models of other metallurgical applications.