# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 6 Field Verification  
Through the optimization of blasting parameters, Scheme B has been identified as the ideal set of blasting design parameters. Therefore, the optimized parameters were incorporated into the blasting design scheme for this mine, and field verification of the blasting optimization effect was conducted. In the mine area, areas with thick magnetite ore veins and relatively intact rock layers were selected for blasting optimization tests. These include platforms 584, 596, and 608, which were chosen to control the influence of varying rock layer properties. Each platform conducted one blasting test, and a total of three blasting results were analyzed. The blasting design scheme is shown in Table 6. The field operations are illustrated in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & \multicolumn{2}{c}{**Characteristic Points**} & \(S\) **(m)** & \(B\) **(m)** & \(q\) **(kg/m\({}^{3}\))** & \(H_{0}\) **(m)** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{A} & Variable Values & 1.000 & 0.447 & 0.848 & 0.529 \\  & & Parameter Values & 5.800 & 4.800 & 0.450 & 1.200 \\ \multirow{3}{*}{2} & \multirow{3}{*}{B} & Variable Values & 0.782 & 0.643 & 1.000 & 0.338 \\  & & Parameter Values & 5.500 & 4.300 & 0.510 & 2.000 \\ \multirow{3}{*}{3} & \multirow{3}{*}{C} & Variable Values & 0.457 & 0.515 & 1.000 & 0.936 \\  & & Parameter Values & 5.000 & 4.000 & 0.550 & 2.500 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Three typical solutions from the Pareto front.  
Figure 9: Two-objective Pareto optimal solution set for fragmentation and cost.  
The results of blasting fragmentation after detonation at platforms 584, 596, and 608 are shown in Table 7. The P\({}_{50}\) for the three blasting tests were 34.27 cm, 33.66 cm, and 35.34 cm, respectively, which were lower than the mine's average blasting block size of 40.91 cm, indicating a significant improvement in the blasting optimization effect. Furthermore, according to mine statistics, the monthly average cost changes before and after optimization are shown in Table 8. Although the blasting monthly cost increased by CNY 67,600 compared to the previous statistics, the total cost of the entire mining operation chain (blasting, loading, transportation, and crushing) decreased by CNY 139,400, resulting in an annual saving of CNY 1,672,800. This is because reducing the blasting fragmentation comes at the cost of increased expenditure. However, reducing the fragmentation degree of the ore blasting can significantly improve loading and transportation efficiency and reduce costs. The crushing and grinding costs during beneficiation will also decrease substantially with the reduction in feed size. Therefore, the on-site blasting tests demonstrated that the proposed blasting parameter design scheme in this study is reliable, enabling the mine to effectively improve blasting performance and reduce overall costs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(D\) (mm) & \(S\) (m) & \(B\) (m) & \(q\) (kg/m\({}^{3}\)) & \(H_{0}\) (m) & \(H\) (m) \\ \hline
140 & 5.5 & 4.3 & 0.51 & 2.0 & 12 \\ \hline \hline \end{tabular}  
Among the variables, D represents the diameter of the blast hole.  
\end{table}
Table 6: Field test blasting design parameters.  
Figure 10: Field operations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Areas** & **584** & **596** & **608** \\ \hline P\({}_{50}\) (cm) & 34.27 & 33.66 & 35.34 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Analysis results of P\({}_{50}\).