A significant proportion of mineral production is derived from surface mining operations, which have witnessed rapid growth due to the deployment of high-capacity equipment. The need to meet rising demand for minerals has led to the extensive use of heavy earth moving machinery (HEMM) such as shovels, excavators, and dumpers. These machines represent substantial capital investments, and their performance must be optimized for cost-effective mining. Among the many factors influencing equipment efficiency, the results of blasting—particularly the fragmentation size, distribution, and muck pile profile—play a critical role in determining the productivity of excavation and loading operations. Therefore, proper blast design is fundamental to the economic success of surface mining projects. Traditional methods such as trial-and-error and cratering are no longer viable for large-scale operations due to inefficiency and unpredictability. While empirical methods remain widely used for estimating blast design parameters, advancements in computer modeling offer promising alternatives that combine precision and adaptability. The integration of empirical formulas, simulation-based approaches, and instrumented field trials can significantly improve fragmentation outcomes. However, computational techniques are still underutilized in routine mine planning. This dissertation focuses on identifying both controllable and uncontrollable parameters that affect surface blast design. Using the well-known model by Langefors and Kihlstrom (1978) as a foundation, a computer model was developed—first in C++ and later migrated to a Java-based platform using NetBeans IDE 6.5. The software incorporates a database and user-friendly interface to aid in predicting fragmentation and optimizing blast design. The model was tested on both coal and iron ore mines, demonstrating reasonably accurate results. This study aims to bridge the gap between theoretical blast design models and practical field applications, ultimately enhancing the synergy between blast fragmentation and shovel efficiency in surface mining operations.
Mr. Jaykant Raidas (Tue,) studied this question.