# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 5 Results and Discussion
### Modeling for Prediction
#### 5.1.2 Comparative Models  
RIME-LSSVM, PSO-LSSVM, LSSVM, and the Kuz-Ram model were developed to compare with the GA-LSSVM model and assess its predictive performance. LSSVM has been described earlier in the text, with \(\gamma\) and \(\sigma^{2}\) taken as (95, 16).  
RIME-LSSVM is an LSSVM model optimized using the time optimization algorithm. By optimizing LSSVM with RIME, it enhances various aspects of the model's performance, including improving prediction accuracy, enhancing generalization capability, boosting robustness, and adapting to complex data structures [45]. This makes LSSVM more efficient and reliable in handling real-world problems. In RIME, the parameter (\(\omega\)) controlling the  
Figure 5: The selection of the maximum number of iterations and population size.  
Figure 6: The regression fitting effect of GA-LSSVM.  
number of segments in the step function is set to 5, resulting in optimized values of \(\gamma\) and \(\sigma^{2}\) as (51, 30), respectively.  
The PSO-LSSVM model combines the global search capability of PSO with the powerful modeling capability of LSSVM, enabling it to achieve good results in many machine learning and optimization problems. Using an intelligent model optimized by particle swarm algorithm to predict mine blasting vibration is considered efficient [30]. In the model construction process, the individual learning coefficient (\(c_{1}\)) and the global learning coefficient (\(c_{2}\)) of PSO were set to (1.5, 1.7), resulting in optimal values of \(\gamma\) and \(\sigma^{2}\) as (81, 75).  
The Kuz-Ram model is widely applied in the prediction of blast fragmentation [4, 46]. The basic formula of the Kuz-Ram equation, which relates the average fragmentation size to the specific charge, is as follows:  
\[P_{50}=K\cdot(\frac{V_{0}}{Q})^{0.8}\cdot(Q)^{\frac{1}{6}}\cdot(\frac{115}{E})^ {\frac{19}{30}} \tag{8}\]  
Among the variables, \(K\) is the rock coefficient, typically taken as 7 for medium rocks, 10 for hard rocks with developed fissures, and 13 for hard rocks without developed fissures; \(V_{0}\) represents the volume of crushed rock per hole; \(Q\) denotes the TNT equivalent energy of explosive charge per hole, which can be obtained by multiplying the line explosive density (\(ql\)) by the explosive length (\(h\)); and \(E\) represents the relative weight power of the explosive, taken as 100 for ammonium nitrate fuel oil and 115 for trinitrotoluene. To achieve the desired blasting effect, a high-velocity emulsion explosive with a cartridge diameter (\(d\)) of 140 (mm) and \(ql\) of 11.4 (kg/m) is used.  
Considering the engineering context, the blasting parameter design for the open-pit iron mine needs to take into account the maximum size of ore blocks after blasting. Therefore, according to reference [47], the parameter of maximum allowable fragmentation size (\(X_{m}\) = 0.7 m) is introduced to modify the following equation:  
\[P_{50}=K\cdot\sqrt{X_{m}}\cdot\frac{V_{0}}{Q}^{0.8}\cdot(Q)^{\frac{1}{6}}\cdot (\frac{115}{E})^{\frac{19}{30}} \tag{9}\]