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By E. Lentilucci, A. Dey, X. Liu, A. Datta, S. Ghosh

The segmentation of quarry blast smoke plumes is a crucial research area that has the potential to enhance safety, environmental monitoring, object detection, and regulatory compliance in the mining industry. Smoke plume segmentation can be used to identify the spatial extent and temporal evolution of smoke plumes generated by the blast. This information can be used to improve the understanding of blast dynamics and optimize blast designs. Furthermore, smoke plume segmentation can assist in identifying potential environmental and health hazards associated with the release of smoke and dust particles into the atmosphere during quarry blasts. However, many current smoke plume segmentation algorithms are designed for segmentation of smoke plumes from fires and are not suitable for segmentation of plumes from quarry blasts. The proposed method in this paper leverages the video frame captured before the blast as a stationary background image, which helps to reduce other confuser objects present in the image along with a selfadaptive clustering method. The color channels of the image are utilized based on the different contrast properties of the smoke, with a self-adaptive gradient compensation applied for edge detection of the smoke plume. This approach offers a novel and effective way to segment smoke in quarry blast analysis, with potential applications in the calculation of noxious gas volume and concentration within the plume. Compared to other algorithms, our method improves accuracy, decreases the false alarm rates, and reduces the overall miss rate. Overall, this paper contributes to the research on smoke plume image segmentation, particularly for quarry blast smoke plumes, which possess unique characteristics that require specialized algorithms for accurate segmentation.

Jan 1, 2024

By Z X. Zhang

If the iron ore loss is 15% in the process of mining, the total iron ore loss in the world will be about 461 million tons, this is equal to the total production of 230 iron ore mines each of which produces 2 million-tons of iron ore per year. Based on a series of blast tests in the Malmberget mine, this paper shows that ore recovery can be markedly increased by improving blasting technology such as choosing a correct primer position and making use of shock wave collision. The paper further suggests that a high ore recovery can not only reduce natural resource loss and mining cost but also prolong the life of a mine. In addition, the paper shows an example of reducing blast-caused rock damage in the roofs and walls of underground drifts (tunnels) by using new blasting methods in the Malmberget mine. Finally, the paper shows how the Malmberget mine has successfully reduced the ground vibrations, without adversely affecting fragmentation and ore recovery in sublevel caving mining.

Jan 1, 2014

By E. Vilaescusa, J. Eloranta

The work presented in this paper investigates the influence of non-ideal detonation characteristics related to the blasthole diameter, base emulsion sensitivity (micro-balloon sensitised versus unsensitised) and percent emulsion on the explosive shock energy available to break rock using ANFOemulsion blends. A series of theoretical equal powder factor bench blasting patterns with hole diameters of 127mm (5”), 140mm (5.5”), 152mm (6”) and 165mm (6.5”) are compared to assess the non-ideal confined shock-state work energies. The Non-Ideal Shock Energy Factor (NSEF), which is the available shock energy per cubic meter of blasted material (MJ/m3) or kilocalories per cubic yard (kcal/yd3), is used to compare the different blasting patterns charged with a range of ANFO-emulsion blends from zero to 50% emulsion.

Jan 1, 2012

By Jorge Blázquez, Pablo Ravanal, Paulo Couceiro, Juan Navarro

Blasting performance is influenced by rock mass properties and blast design parameters. From them, the rock is the only variable that cannot be modified, but it can be measured, so that, blast design variables can be adapted to optimize the results. A new methodology to assess rock mass quality from drill-monitoring data from both autonomous and manual rotary drilling modes is described to guide blasting in open pit operations. For that, a thorough normalization process has been developed to minimize external influences induced in the data by the rig system for autonomous mode and by the operator intervention for manual mode. Principal component analysis has been used to combine MWD information. Two rock description indexes are derived directly from Measurement While Drilling (MWD) data collected. The first index is a Structural factor that classifies the rock mass condition in three classes (massive, fractured and heavily fractured). The second index is a Strength factor that classifies the rock according to the strength properties. Both indexes have been combined to obtain an overall drilling rock factor along the hole. This has been named by the authors as the X-Rock model.

Feb 6, 2023