Abnormality monitoring and causality analysis based on KF-PDC and IACE in blast furnace ironmaking process

ABSTRACT

Blast furnace (BF) ironmaking is a highly complicated process with multi-variable nonlinear coupling and multi-mode characteristics. In this article, a developed kernel function partial derivative contribution (KF-PDC) is proposed for abnormality location, which makes up for the deficiency of linear multivariate statistical process monitoring (MSPM) and de-redundancies the variable candidate set of causality analysis. Then, to eliminate the influence of multi-mode, an online interval adaptive causation entropy (IACE) is established to analyse the cause–effect relationships of candidate abnormal variables, which contributes to distinguishing the direct and indirect causality, and the Haar wavelet based on the sliding window (HWSW) is constructed for the segmentation of different modes online. Finally, a case study using actual industrial BF ironmaking data illustrates that the monitoring method can better capture the abnormal furnace conditions and effectively obtain the root cause and propagation path.

KEYWORDS:

• Blast furnace
• nonlinear coupling
• multi-mode
• process monitoring
• mode segmentation
• Haar wavelet
• transfer entropy
• causality analysis

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (U1908213), in part by the Colleges and Universities in Hebei Province Science Research Program (QN2020504).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number U21A20475]; National Natural Science Foundation of China [grant number U1908213].