Optimal blending strategies for coking coal using chance constraints

Abstract

Coking coal is essential for the production of steel, and the quality of this coal significantly contributes to the quality of the produced steel. High quality coking coal has low ash content and a range of properties including volatile matter content and predicted coke strength. The coal is improved by processing after it has been mined. This processing varies and coal from multiple sources is blended. This paper introduces an original mixed integer programming model to maximise the profit of coal blending and processing. The model is computationally efficient and can be implemented at any coal mining and processing operation. The multi-period blending model incorporates stockpiling of raw material, and explicitly captures the geological variability of coal using chance constraints. A case study is evaluated and demonstrates that explicitly modelling geological variability can reduce the risk of breaching product specifications without any revenue loss. The improvement is achievable, without additional cost, by selecting the order that coal is fed into a processing plant.

Keywords:

• Stochastic programming
• chance constraints
• mining optimisation
• coal blending

Acknowledgements

The authors would like to thank BHP for providing the data set used in this study and also for the time allowed to the first author. The authors also thank the reviewers for their valuable comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability

The scaled data sets supporting the case study and the Python code are available by contacting the corresponding author.