# Blasting Fragmentation Study Using 3D Image Analysis of a Hard Rock Mine
## 3 Results and Discussion
Even so, it is possible to observe that the proportionality of the results is maintained with the mathematical calculation, thus making it a form of significant analysis when validating the blasting. An applied study similar to the one provided in this article can be seen by [PERSON] (2018), who used the neural network technique to optimize rock fragmentation induced by detonation. For the same to happen in this study, instead of using PortaMetrics, Split-Desktop was used, with the test verifying the same things, including a distribution variation between 15 and 40 cm, with this being possible due to using the neural network tools, a good correlation, and obtaining the rock fragmentation in an optimized way. Based on the equations presented in Table 4 and information from Tables 2 and 5, the yield indicator related to X80 obtained in each test performed was determined to evaluate the impacts of the quality of blasting fragmentation in the subsequent operational phases. Table 6 presents the operating parameters used to calculate the yield. These parameters were obtained from a database provided by the mine, which was processed and statistically analyzed. Figure 6a,b and Figure 7 show the productivity estimates of the loading, transport, and primary crushing phases relative to the X80 obtained in each blasting test performed in this study. Figure 5: Kuz–Ram and MMPro particle size distributions for all tests. As expected, the productivity levels of operations were inversely proportional to the sizes of the blasting fragments, specifically studied by X80 in this case. When considering each blasting and its parameters, Tests 1-4 showed a satisfactory improvement in productivity relative to conventional blasting. Because it was performed with a higher load ratio, Test 1 showed a satisfactory improvement in productivity relative to conventional blasting. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{5}{c}{**Tests**} \\  & **1** & **2** & **3** & **4** & **Conventional** \\ \hline \(l_{C}\) & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \(L_{c}\) & 40.76 & 40.76 & 40.76 & 40.76 & 40.76 \\ \(a\) & 12.15 & 12.15 & 12.15 & 12.15 & 12.15 \\ \(t_{sw}\) & 21.67 & 21.67 & 21.67 & 21.67 & 21.67 \\ \(t_{d}\) & 3.95 & 3.95 & 3.95 & 3.95 & 3.95 \\ \(U_{t}\) & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ \(L_{t}\) & 243.00 & 243.00 & 243.00 & 243.00 & 243.00 \\ \(p\) & 6.00 & 6.00 & 6.00 & 6.00 & 6.00 \\ \(t_{v}\) & 23.62 & 23.62 & 23.62 & 23.62 & 23.62 \\ \(t_{f}\) & 6.38 & 6.38 & 6.38 & 6.38 & 6.38 \\ \(P_{bp}^{\prime}\) & 3523.00 & 3523.00 & 3523.00 & 3523.00 & 3523.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Operating parameters used to estimate the productivity of the loading, transport, and primary crushing phases. Figure 6: (a) Loading productivity and (b) transport productivity relative to \(X_{80}\). Figure 7: Primary crushing productivity relative to \(X_{80}\). 3 presented a better fragmentation result and, consequently, the best-estimated values for the productivity of the other steps. The mine productivity with conventional blasting was not the most adequate, considering the X80 obtained via the test performed in this study. However, when considering the operating costs, the maximum productivity estimated here that was related to fragmentation did not correspond to the lowest cost of any of the mine phases [32]. Some scholars have evaluated the impacts of fragmentation on productivity in loading and transport and on the energy consumption used by crushing [33]. This finding was especially true for mines with very compact ore, such as the mine under study, where there was increased pressure to improve fragmentation without significantly increasing the blasting costs [34]. What could happen when adopting the configuration of the blasting plane of Test 3, in which a greater level of explosives was used? Based on the visual analysis in the field, it was possible to identify a relative improvement in the fragmentation of HCI.