Development of artificial neural network based mathematical models for predicting small scale quarry powder factor for efficient fragmentation coupled with uniformity index model

Abstract

Blasting is the primary method for reducing rock size in small-scale mining operations. The primary purpose of blasting is to assist rock mass reduction and transportation from the mine to the processing facility. The explosive charge utilized in this blasting operation has an impact on production output, safety, and profitability. The explosive powder factor is used in mining to calculate the quantity of explosive required per mass of rock fragmented, and this study looks at how different powder factors affect blast fragmentation. A machine learning approach was used in this study to optimize explosive use in small-scale quarries. This study employed data from a small-scale dolomite quarry in Akoko Edo, Nigeria, for artificial neural network (ANN) modeling and uniformity index model creation (10 production blasts and 38 blast record datasets). Powder factors greater than 0.7–0.8 kg/m3 result in a lower uniformity index, according to an analysis of monitored blast results. The results showed that powder factors of 0.7 kg/m3 (between 1.6 and 1.7) had the highest uniformity index. According to the findings, the small-scale optimum blast uniformity index is between 1.33 and 1.68. The proposed ANN model performs well in terms of prediction accuracy, as determined by five error indices with coefficients of correlation (R2) of 0.997 on the training dataset and 0.97 on the testing dataset. Based on the model performance analysis results, the suggested ANN model can be used to improve the small-scale blast powder factor in actual applications.