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%.
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