Prediction of flyrock distance in open-pit mines using an optimized artificial neural network with evolution strategies

Tóm tắt

Blasting is a fundamental technique in open-pit mining, used to break rock and ore. Its effectiveness and the degree of fragmentation significantly affect the efficiency of subsequent processes and the overall mine productivity. However, a major concern isthe dangerous impact of flyrock, which poses serious safety risks to personnel and equipment in the vicinity, potentially leading to fatal accidents. This paper presents an advanced machine learning model, named ES-ANN, which combines an Artificial NeuralNetwork (ANN) with Evolution Strategies (ES) to predict flyrock distance in open-pit mines with high accuracy. The ANN model is used to forecast flyrock distances, while the ES technique optimizes the model's weights, enhancing prediction accuracy. To evaluate the improvement of the proposed ES-ANN model, another optimization model based on the Evolutionary Programming (EP) optimization algorithm and ANN (abbreviated as EP-ANN), and a standalone ANN model were developed and compared based on the same datasets. Blasting data from the Ta Phoi copper mine (Lao Cai) was utilized for model training and validation. The results indicated that the ES-ANN model achieved the highest performance with an MAE of 2.095, RMSE of 2.711,and R2of 0.952 on the testing dataset (95.2% accuracy) in predicting flyrock distance. Meanwhile,the EP-ANN and standalone ANN models only provided MAE of 5.512 and 7.300, RMSE of 6.692 and 8.938, and R2of 0.708 and 0.479, respectively. Compared to the EP and traditional methods, theES-ANN model offered superior accuracy and reliability, making it an effective tool for forecasting and managing flyrock hazards in open-pit mining, thus enhancing operational safet