基于高斯混合模型的MWPCA高炉异常监测算法

大型高炉是钢铁制造过程中的重要装备，由于高炉运行过程复杂，干扰因素繁多，经常会有异常炉况发生。为及时监测异常炉况、保证高炉顺行，本文利用高炉运行数据，开发了一种基于MWPCA和高斯混合模型的算法对高炉异常过程进行监测。由于高炉运行数据存在非高斯分布和时变的特点，利用高斯混合模型改进了传统PCA监测模型的T²统计量，使算法可以适应高炉数据的独特分布特征，并加入了滑窗机制，使算法具有实时更新的能力。随后，将算法应用在华南某大型钢铁集团的真实高炉数据上，检测了算法的有效性，并将其与基础算法进行了对比分析，证明了算法在高炉异常监测能力上有所提高。

MWPCA blast furnace anomaly monitoring algorithm based on Gaussian mixture model

Large-scale blast furnaces are important equipment in the steel manufacturing process. Due to the complex operation of the blast furnaces and the many interference factors, abnormal furnace conditions often occur. In order to monitor the abnormal furnace conditions in time and ensure the blast furnace is running smoothly, this paper develops an algorithm based on the principal component analysis and gaussian mixture model to monitor the abnormal process of the blast furnace. Due to the non-Gaussian distribution and time-varying characteristics of blast furnace operating data, the Gaussian mixture model is used to improve the T² statistics of the traditional PCA monitoring model, so that the algorithm can adapt to the unique distribution characteristics of blast furnace data. And the sliding window mechanism is added to give the algorithm the ability to update in real time. Subsequently, the algorithm was applied to the real blast furnace data of a large iron and steel group in South China. The effectiveness of the algorithm was tested and compared with the basic algorithm to prove the improvement of the algorithm's ability to monitor blast furnace anomalies.