| Abstrakt: |
The comparative analysis of predictive methodologies highlights the original contribution of this study in optimizing the prediction of Rate of Penetration (ROP) in mining drilling operations. The emphasis on employing advanced Artificial Neural Networks (ANN), fuzzy logic, and linear regression models provides new insights into enhancing predictive accuracy and operational efficiency in mining practices. This study aims to quantify the effects of three pivotal drilling parameters: compressive strength, rotational pressure, and thrust pressure on the rate of penetration, a critical performance metric in mining drilling operations. Additionally, the study seeks to develop and evaluate advanced predictive methodologies for predicting ROP. The effects of compressive strength, rotational pressure, and thrust pressure on the rate of penetration were investigated through a Design of Experiments (DOE) approach. Initially, the main effects and two-way interactions among these variables were identified using DOE. Subsequently, three predictive methodologies: linear regression, fuzzy logic, and artificial neural networks, were developed and evaluated to predict ROP based on the identified factors. The evaluation of predictive methodologies revealed that the ANN model demonstrated superior accuracy in predicting the ROP, achieving over 95 % accuracy. Additionally, the fuzzy logic model provided effective handling of nonlinearities in the data, while the linear regression model offered initial insights into the relationships between the variables. The application of advanced predictive methodologies: artificial neural networks, fuzzy logic, and linear regression to optimize the prediction of rate of penetration in mining drilling operations offers precise insights into drilling parameter interactions, enhancing operational efficiency and supporting informed decision making in mining practices. [ABSTRACT FROM AUTHOR] |
