A static end-point prediction model based on automatic optimization is proposed to predict the end-point carbon content and temperature of the basic oxygen furnace (BOF) steelmaking. Initially, redundant features in the samples are eliminated through mechanistic analysis, and violin plots are employed to minimize the impact of outliers. Subsequently, Spearman correlation analysis is utilized to identify statistically significant features influencing the end-point, hence constructing a highly interpretable feature set. Finally, leveraging the advantages of the lévy-flying algorithm (LWOA), which offers simplicity in parameter tuning and strong global search capabilities, the parameters of the projection wavelet weighted twin support vector regression (PWWTSVR) objective function are optimized. These parameters include the regularization parameter c 1 , c 2 , c 3 , c 4 , c 5 , c 6 , the insensitivity parameter ε 1 , ε 2 , the width of the radial basis function σ , the amplitude E, and the standard deviation γ . Additionally, by setting different population sizes (1, 20, 50, 100), the influence of the whale population size in LWOA on the predictive model is investigated. Simulation results show that the prediction model achieves hit rates of 94% for end-point carbon content and 97% for temperature, with error tolerances of ±0.005 wt.% and ±10 °C, respectively. The dual hit rate stands at 91%, outperforming five other models. Furthermore, the predictive capability of the proposed model is comprehensively evaluated by implementing various error intervals, hence validating its effectiveness.

Search results

A static prediction model of end-point carbon content and temperature of BOF steelmaking based on automatic optimization