Research on key indicators prediction of steel process adapting to operating condition deviation

Abstract

This study developed an indicator prediction model for steel processes adapting to dynamic operating condition deviations. By combining just-in-time learning, ensemble learning techniques, and target similarity extraction, the model improves prediction accuracy and robustness. Validated on industrial rolling and sintering data, the model achieves up to 18% improvement over traditional models. Utilizing multi-dimensional parameters, the 6% error margin hit rates for yield and tensile strength reached 86% and 99% in rolling data, while the Al₂O₃ hit rate reached 96% in sintering. Notably, for boundary samples (top/bottom 25% quantiles), the model achieved a 56% relative improvement in hit rate. Furthermore, the model significantly enhances prediction robustness under dynamic operating deviations, reducing the accuracy fluctuation across time segments from 12% to 5%.