BENCH BLASTING DESIGN OPTIMIZATION USING MULTI-OBJECTIVE GENETIC ALGORITHM (MOGA)

• Main Author: Sasi Maulidya, Juwita
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/57372
• Institution: Institut Teknologi Bandung

The blasting design in several mines only uses an empirical approach (R.L Ash or Konya) as a reference, and trial and error are carried out to obtain the desired fragmentation. Optimal fragmentation is required so that there are no fragment sizes that are too large (secondary blasting or rock breaker is required) or fragment sizes that are too small (reduced recovery volume) which can reduce productivity. Therefore, an optimization method is needed to produce optimal fragmentation, uniformity index, and PPV value. One of the optimization methods is Multi-Objective Genetic Algorithm (MOGA) with the average fragmentation value and uniformity index as the objective function to obtain an explosion design that is able to produce optimal fragmentation values quickly with a small fitness value (<0,5) so that a research entitled " Blasting Design Optimization Using Multi-Objective Genetic Algorithm (MOGA)" was made. The value of burden, space, subdrill, and stemming is obtained for each location and the excavator used by using the MOGA optimization application with fitness value < 0,5 with a relatively fast time (average 12 seconds). Data validation shows the average value of Mean Absolute Percentage Error (MAPE) between the Kuz-Ram formula with the image analysis method with split-desktop aplikasi using data from a gold mine in West Sumbawa is 3,22%, which means the Kuz-Ram model is good for calculating the value of rock fragmentation from blasting activity. There is a difference of up to 1,7 meters in the blasting design used by one of the gold mines in West Sumbawa to the optimized blasting design, burden and space in optimization design has wider spacing, making it more profitable