Estimation of Drilling Machine Production Rate Based on Properties of Rock and System Parameters by Prediction of Bit Penetration Speed

Drilling a certain number of blast holes per hour plays an important role in reaching the required annual mine production. Since, the proper use of the availability time of machine to drill the maximum number of blast holes is crucial. The drilling rate of blast holes is affected by various factors such as rock properties and system parameters. Thus, recognizing the effectiveness of these factors on the penetration rate of bit (PR), not only machine production can be increased but also drilling and blasting costs can be reduced in the mine. In this study to predict the PR in the selected mine, firstly, parameters of 91 holes related to 28 blasting block and in 9 various extracting benches were collected. Secondly, the sensitivity rate related to each of the independent parameters on the PR was studied using Cosine Amplitude Method (CAM). Finally, three models including non-linear multivariate regression (NLMR), artificial neural network (ANN), and fuzzy logic were produced to predict the PR. These models were validated using 12 series of data tests. It was shown that with a coefficient of determination of 0.68 and mean absolute percentage error (MAPE) of 12.15, the ANN model could predict the PR with a slightly higher precision compared to NLMR.