用混合量子神经网络提升炼铁炉温预测与控制精度
Predictive control of blast furnace temperature in steelmaking with hybrid depth-infused quantum neural networks
- 结合量子计算与经典回归模型，增强特征空间探索能力
- 温度预测准确率提升超25%，控制波动从±50℃缩至±7.6℃
- 适合关注工业智能控制与量子机器学习融合的读者
准确预测并稳定炼铁高炉温度对提升钢铁生产效率和产能至关重要。传统方法难以应对高炉内温度变化的复杂非线性特性。本文提出一种新型混合量子机器学习方法，结合粉煤喷吹控制以解决上述挑战。通过将经典机器学习技术与量子计算算法融合，提升预测精度并实现更稳定的温度调控。采用基于预测的优化策略，利用量子增强的特征空间探索能力与经典回归模型的鲁棒性，预测温度波动并优化粉煤喷吹量。实验结果表明，预测准确率提升超过25%，温度稳定性由原先的±50℃改善至±7.6℃以内，验证了混合量子机器学习模型在工业钢铁生产中的应用潜力。
原文摘要 · Abstract (English)
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.
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