HHO–LSSVM prediction model of blast casting muck pile morphology based on Gaussian distribution - Ning MA; Li MA; Yinda LI; Tianxiang LI; Sen YANG; Fuming LIU; You ZHOU; Xiaomin WANG; Qifeng ZHANG; Mengbo LI (Meitan xuebao)
The shape of the blast casting muck pile in surface coal mines is an important factor that affects the operational efficiency and cost of the blast casting pulling shovel stacking process system. In order to improve the accuracy of predicting the shape of blast casting muck piles in surface coal mines, further optimize the design of blast casting, and reduce the cost of blast casting stripping, based on the measured data of blast casting in the Heidaigou surface coal mine, an example analysis was conducted. The entropy method grey correlation method was used to study the weight and correlation between the impact indicators of blast casting effect and the farthest throwing distance, looseness coefficient, and effective throwing rate. The correlation between hole spacing, section width and the evaluation indicators of blast casting effect is relatively low. The minimum resistance line, row spacing, explosive consumption, step height, lower mouth width of goaf, slope angle, and upper mouth width of goaf were selected as input parameters for the prediction model. The Gaussian distribution model was introduced to simulate the profile curve of the blast casting muck pile. By using a 1–8th order Gaussian distribution model to simulate and analyze the detonation profile curve, it was determined that the simulation accuracy and efficiency reach an optimal level when the order is 5. The trained HHO–LSSVM was used to predict 15 parameters of the 5th order Gaussian distribution model, which were used as the control parameters for predicting the output detonation shape. And the Gaussian distribution model combined with the HHO–LSSVM algorithm was used to predict the shape of the blast casting muck pile, compare the accuracy of the LSSVM, Particle Swarm Optimization (PSO) optimized Least Squares Support Vector Machine, and Genetic Algorithm (GA) optimized BP neural network models, at the same time, compare the predicted blast casting muck pile morphology with the actual blasting muck pile morphology. The results show that the sum of squared errors (S) parameter of the blasting muck pile morphology curve simulated using a 5th order Gaussian distribution tends to stabilize at 25.69, and the coefficient of determination (R2) and adjusted coefficient of determination (\begin{document}$R_{A}^2 $\end{document}) are 0.999 2 and 0.999 0, respectively. The root mean square error (R) is 0.514 6. The prediction error of the HHO–LSSVM for the 5th order Gaussian distribution control parameters is mostly around 1%, and the error does not exceed 5%, compared with the LSSVM, PSO, and GA–BP algorithm models, the accuracy is higher. Taking the profiles E5–8, E5–9, and E5–10 as examples, the errors R2 and R between the predicted and actual detonation morphology are 0.998 7 and 0.614 2, 0.999 2 and 0.493 1, 0.999 2 and 0.505 2, respectively, the predicted shape of the blasting muck pile is close to the actual shape of the blasting muck pile morphology.