Causality analysis and prediction of blast furnace state based on convergence cross mapping

ABSTRACT

Blast furnace (BF) ironmaking system is a complex industrial system so this paper proposes a BF state causality analysis method based on the use of convergent cross-mapping method (CCM). This method can accurately describe the causal relationships between states at different locations in the BF system. It can also be used as a feature selection method for prediction models. After obtaining accurate causal characteristics of the BF state covariates, the BF system process theory is used for validation. The causal characteristics are used as input variables to the extreme gradient boosting model (XGboost) for predicting BF state parameters. After testing with industrial data, the model predicted an absolute error control within 2% with an accuracy of over 88%. The CCM approach mentioned in this paper is more suitable for state causal impact analysis and predictive model feature selection for BF systems.

KEYWORDS:

• Convergent cross-mapping
• Blast furnace state analysis
• XGboost
• Causal analysis

Acknowledgements

Thanks are given to the financial support from the Basic Research Program of the National Nature Science Foundation of China (52004096), the China NSF project (E2019209314), and Hebei Provincial Higher Education Fundamental Research Projects (JQN2020032).

Disclosure statement

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

Additional information

Funding

This work was supported by the Basic Research Program of National Nature Science Foundation of China [grant number 52004096]; Hebei Provincial Higher Education Fundamental Research Projects [grant number JQN2020032]; the China NSF project [grant number E2019209314].