Accurate prediction and stabilization of blast furnace temperatures are crucial for optimizing the efficiency and productivity of steel production. Traditional methods often struggle with the complex and non-linear nature of the temperature fluctuations within blast furnaces. This paper proposes a novel approach that combines hybrid quantum machine learning with pulverized coal injection control to address these challenges. By integrating classical machine learning techniques with quantum computing algorithms, we aim to enhance predictive accuracy and achieve more stable temperature control. For this we utilized a unique prediction-based optimization method. Our method leverages quantum-enhanced feature space exploration and the robustness of classical regression models to forecast temperature variations and optimize pulverized coal injection values. Our results demonstrate a significant improvement in prediction accuracy over 25 percent and our solution improved temperature stability to +-7.6 degrees of target range from the earlier variance of +-50 degrees, highlighting the potential of hybrid quantum machine learning models in industrial steel production applications.

翻译：高炉温度的准确预测与稳定控制对于优化钢铁生产效率与产能至关重要。传统方法往往难以应对高炉内温度波动所固有的复杂非线性特性。本文提出一种新颖方法，将混合量子机器学习与喷煤控制相结合以应对这些挑战。通过融合经典机器学习技术与量子计算算法，我们旨在提升预测精度并实现更稳定的温度控制。为此，我们采用了一种独特的基于预测的优化方法。该方法利用量子增强的特征空间探索能力以及经典回归模型的鲁棒性来预测温度变化并优化喷煤量。实验结果表明，预测精度较传统方法提升超过25%，温度稳定性从原有的±50度目标范围波动改善至±7.6度，凸显了混合量子机器学习模型在工业钢铁生产应用中的巨大潜力。