# Prediction of Blast Crushing Lumpiness Based on CPO-BP Modeling
## 6 Conclusions  
In this study, the model was trained and tested using the Hudaverdi blasting dataset, which is widely used in rock blasting research and includes data from various geological and operational settings. This ensures that the model learns from a broad set of blasting scenarios rather than being limited to a single mine. To further validate its applicability, the model was applied to a real-world case study involving an iron ore mine in Hunan Province, China. The results from this engineering application confirmed the model's ability to generalize beyond the training data and accurately predict blast fragmentation under different geological conditions. These findings suggest that the CPO-BP model has strong potential for application across diverse rock types and mining operations.  
While the CPO-BP model demonstrates promising performance, there remains room for enhancing the methodological robustness through integration with other complementary techniques. For instance, ensemble learning methods or hybrid metaheuristic combinations could further improve stability and generalization. Additionally, exploring deep neural network architectures or model pruning strategies may also enhance efficiency and predictive power. These directions will be considered in future work.  
The CPO-BP prediction model is based on the basic principles of BP neural network and CPO algorithm, based on an iron ore mine in Hunan province, using the CPO algorithm to optimize the weights and bias of the BP neural network and combining with the typical blasting data for the training and testing of the model, so as to validate the reliability of the model in predicting the blasting block size and practical engineering applications. It is summarized as follows:  
1. The CPO-BP model was trained and tested using the Hudaverdi blasting dataset, a representative and widely used database in blasting research. To verify its practical utility, the model was also applied to a real-world iron ore mine in Hunan Province, China. Results from both the benchmark dataset and the field validation confirm that the model can generalize well across different blasting conditions and deliver high prediction accuracy, indicating its potential for broad engineering application.
2. Although the model demonstrated high accuracy on the current dataset, its generalization ability across different geological conditions and blasting environments remains to be verified. Future research will explore applying the CPO-BP model to other datasets to further evaluate its robustness and practical transferability.  
Conceptualization, C.H. and X.X.; methodology, C.H. and X.X.; software, C.H.; validation, C.H. and X.X.; formal analysis, C.H.; investigation, C.H.; resources, C.H. and X.X. data curation, C.H.; writing--original draft preparation, C.H.; writing--review and editing, X.X.;visualization, C.H.; supervision, X.X.; project administration, X.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. If any researcher is in need of the data and codes, email: [EMAIL_ADDRESS].  
The authors declare no conflicts of interest.