Publications internationales
Résumé: 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.
Résumé: The focal objective of optimizing drilling processes is to mitigate challenges tied to the operation. However, the triumph of mineral drilling relies on the availability of pertinent data to ensure effectiveness. For efficient and successful drilling, an optimization approach necessitates access to pertinent data, especially concerning the physicochemical properties of the rock and operational parameters of the machine. In this study, our focus is on optimizing specific energy, a critical metric for assessing mining drilling efficiency. This measure evaluates the energy used during drilling per unit volume of rock extracted. Considering the complexity of factors involved, treating the selection of the operational mode governing specific energy as a form of multi-criteria decision-making is justifiable. This method involves an in-depth analysis of the problem's underlying structure. Experimental measures were used to validate the proposed optimization approach. The paper delves into evaluating the differences in rankings derived from the TOPSIS and VIKOR methods. A ranking similarity coefficient is employed to compare the rankings against experimental values. Ultimately, the available choices are prioritized, and the most suitable operating mode for the drilling machine is determined. The study's comparative analysis using TOPSIS and VIKOR methodologies leads to the discovery of the best operational modes for drilling machines, highlighting the subtle differences in how well the two methods work. By using a ranking similarity coefficient, this study not only shows what each method's rankings mean in real life compared to experimental values, but it also gives a plan for improving the efficiency of drilling machines by carefully adjusting their parameters. Such insights contribute significantly to the field of drilling optimization, showcasing a methodical approach to energy conservation and operational efficiency.