Method for Optimizing Block Size in Open-Pit Mining Operations

A block model's precision determines the production plan's effectiveness in open-pit mining operations. Therefore, identifying the most suitable approach and dimensions for dividing the block model is paramount. In a recent article published in Scientific Reports, researchers from China focus on developing a novel method for determining the block size in open-pit mines. The block model is crucial for various tasks in open-pit mining, such as production planning, reserve estimation, and mine design.

open pit mining

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Open-pit mining traditionally relies on regular block models filled within a three-dimensional geological solid model. The size of these blocks is determined based on various factors, such as mining method, ore selectivity, and economic considerations.

Previous studies have highlighted the importance of block size selection in optimizing mining operations and economic outcomes. However, practical limitations and the need for accuracy in representing the geological body have been challenging.

The Current Study

The study introduced a novel method for determining block size in open-pit mines by directly cutting a closed-shell three-dimensional geological model. This approach integrated mining parameters and shovel-truck operation efficiency to optimize block model size. The dimensions of the block model were defined based on key factors: bench height for the Z-axis size, bench slope angle for the inclination angle, and shovel width for the X-axis size.

To account for the operational efficiency of the shovel-truck system, the study incorporated a probabilistic analysis method. This method considered the fluctuation in the number of operational trucks and shovels around a certain mathematical expectation. By analyzing the daily production efficiency of each mining equipment, the study determined the volume of individual mining blocks in the field, influencing the determination of the Y-axis block model sizes for different shovels.

The research utilized the Baorixile open-pit mine as a case study to implement the proposed method. A "Mining model" function module was developed within the self-developed "Life Cycle Mining Software System" using the C++ programming language. This module successfully constructed the block model for the case study, demonstrating the practical implementation of the new method.

Furthermore, the study conducted an accurate comparative analysis comparing the closed shell model with traditional block models over four years. The analysis evaluated the precision and efficiency of the proposed method in volume estimation and production planning. The methodology highlighted the advantages of the new approach in reducing errors and aligning with practical mining conditions.

The methodology systematically integrates geological modeling, mining parameters, equipment efficiency, and probabilistic analysis to optimize block size determination in open-pit mines. The technical details and implementation of the method showcased its potential for enhancing production planning accuracy and operational efficiency in mining operations.

Results and Discussion

Comparative analysis with traditional block models demonstrates the proposed method's superiority in terms of accuracy and efficiency. By utilizing the closed shell model and considering mining parameters, the new approach reduces errors in volume estimation and aligns closely with practical mining conditions. The study also emphasizes the impact of block size on production planning and economic outcomes in open-pit mining operations.

The comparative analysis between the closed shell model and traditional block models at the Baorixile open-pit mine revealed significant improvements in accuracy and efficiency with the proposed method. The study demonstrated that the new approach effectively reduced errors in volume estimation and production planning, aligning more closely with practical mining conditions.

By directly cutting the closed shell model and integrating mining parameters, the method provided a more accurate representation of the geological body, leading to enhanced precision in block size determination. The probabilistic analysis method further contributed to capturing fluctuations in equipment efficiency, ensuring a more realistic assessment of operational capabilities. These findings underscore the superiority of the proposed method in optimizing block size for open-pit mining operations, ultimately enhancing production planning and economic outcomes.


In conclusion, the study's innovative method for determining block size in open-pit mines represents a significant advancement in mining engineering. By incorporating geological modeling, mining parameters, and equipment efficiency considerations, the new approach offers a more precise and practical solution for optimizing production planning throughout the life cycle of open-pit mines. The results from the case study at the Baorixile open-pit mine demonstrate the method's effectiveness in reducing errors and improving accuracy in volume estimation.

The research aims to refine the optimization process further and explore additional mining technologies to enhance operational efficiency and economic benefits in open-pit mining operations. The study lays a solid foundation for future advancements in mining industry planning methodologies and decision-making systems.

Journal Reference

Guo, W., Liu, G., Li, J. et al. (2024). Research on the method of determining the block size for an open-pit mine integrating mining parameters and shovel-truck’s operation efficiency. Scientific Reports 14, 10119.

Dr. Noopur Jain

Written by

Dr. Noopur Jain

Dr. Noopur Jain is an accomplished Scientific Writer based in the city of New Delhi, India. With a Ph.D. in Materials Science, she brings a depth of knowledge and experience in electron microscopy, catalysis, and soft materials. Her scientific publishing record is a testament to her dedication and expertise in the field. Additionally, she has hands-on experience in the field of chemical formulations, microscopy technique development and statistical analysis.    


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