Estimation of rock drillability based on a Bayesian multi-branch model

Key words:  rock drillability  Bayesian classifier  Levenberg-Marquardt algorithm  improved double-chain quantum genetic algorithm

Abstract:

A two-level model was established for predicting rock 's drillability based on a Bayesian multi-branch model in order to improve the real-time calculating capability of the model and increase its generalization ability for intelligent optimization control. By using the Bayesian method for lithology classification, the correlations of different rock samples and their drillability can be refined, and consequently the rock drillability model can be improved. Using an optimized back-propagation neural network(BPNN) with an improved double-chain quantum genetic algorithm(IDCQGA), the new model of IDCQGA_BPNN can be modified according to the lithology type of rocks. The results show that this method can not only enhance the generalization ability of the model, which is optimized by an intelligent algorithm, but also can accelerate its calculation speed and improve its accuracy. The simulation results indicate that the model is satisfied for the use in real-time intelligent optimization control process for predicting the rock drillability while drilling.