Optimization of postblast ore boundary determination using a novel sine cosine algorithm-based random forest technique and Monte Carlo simulation

Abstract

The accurate determination of postblast ore boundaries can significantly help to control ore loss and dilution in opencast mines. Determining the boundaries is difficult using methods other than direct and expensive blast-induced rock movement monitoring, so many mines directly use the preblast ore boundary to guide the shovel. A new postblast ore boundary determination method using a soft computing technique and stochastic modelling method is proposed. Based on a case study and performance comparison, a high-precision hybrid metaheuristic model combined with the sine cosine algorithm and random forest technique (SCA-RF) was developed and used in a Monte Carlo simulation to analyse the probability distribution and parameter sensitivity. Mining engineers can obtain a more accurate postblast ore boundary by moving the preblast ore boundary toward the free face by a certain distance after considering the probability distribution of blast-induced rock movement, which is significantly better than using the preblast ore boundary.

KEYWORDS:

• Postblast ore boundary
• blast-induced rock movement (BIRM)
• sine cosine algorithm (SCA)
• random forest (RF)
• Monte Carlo simulation

Acknowledgements

The financial support from the National Natural Science Foundation Project of China [grant numbers. 41807259 and 51874350], the National Key R&D Program of China [2017YFC0602902], the Fundamental Research Funds for the Central Universities of Central South University [2018zzts217] and the Innovation-Driven Project of Central South University [2020CX040] is gratefully acknowledged.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Funding was received from the National Natural Science Foundation of China [Xiuzhi Shi, grant number 51874350]; the National Natural Science Foundation of China [Jian Zhou, grant number 41807259]; the National Key R&D Program of China [Xiuzhi Shi, 2017YFC0602902]; the Fundamental Research Funds for the Central Universities of Central South University [Zhi Yu, 2018zzts217]; the Innovation-Driven Project of Central South University [Jian Zhou, 2020CX040].