A machine learning framework for predicting elastic properties of sedimentary rocks from ball mill grinding characteristics data

Abstract

Elastic properties of rocks like Young’s modulus and compressional P-wave velocity are vital for understanding their stress-strain response in mining and rock engineering applications. Traditional methods for determining these properties involve labor-intensive, expensive and time-consuming. To address these challenges, this study proposes a novel predictive method. It utilizes a multi-layer perceptron feed forward neural network (MLP-FFNN) trained on grinding characteristics of ball mill to predict Young’s modulus and compressional Pwave velocity in sedimentary rocks. Laboratory experiments on limestone and dolomite samples generated extensive data, enabling development of prediction models using the proposed MLPFFNN. The developed models demonstrate high predictive accuracy (R values: 0.952 for E, 0.987 for Vp) in training and good generalization (0.866 for E, 0.9707 for Vp) in testing, along with low Root Mean Squared Error (RMSE) values. These findings underscore the efficacy of neural network models in predicting E and Vp from grinding characteristics of ball mill. © 2024 The Author(s).