Quantifying particle size and size distribution of mine tailings through deep learning approach of autoencoders

Artificial intelligence-based prediction model for the elemental occurrence form of tailings and mine wastes

With the advent of the second industrial revolution, mining and metallurgical processes generate large volumes of tailings and mine wastes (TMW), which worsens global environmental pollution. Studying the occurrence of metal and metalloid elements in TMW is an effective approach to evaluating pollution linked to TMW. However, traditional laboratory-based measurements are complicated and time-consuming; thus, an empirical method is urgently needed that can rapidly and accurately determine elemental occurrence forms. In this study, a model combining Bayesian optimization and random forest (RF) approaches was proposed to predict TMW occurrence forms. To build the RF model, a dataset of 2376 samples was obtained, with mineral composition, elemental properties, and total concentration composition used as inputs and the percentage of occurrence forms as the model output. The correlation coefficient (R), coefficient of determination, mean absolute error, root mean squared error, and root mean squared logarithmic error metrics were used for model evaluation. After Bayesian optimization, the optimal RF model achieved accurate predictive performance, with R values of 0.99 and 0.965 on the training and test sets, respectively. The feature significance was analyzed using feature importance and Shapley additive explanatory values, which revealed that the electronegativity and total concentration of the elements were the two features with the greatest influence on the model output. As the electronegativity of an element increases, its corresponding residual fraction content gradually decreases. This is because the solubility typically increases with the solvent's polarity and electronegativity. Overall, this study proposes an RF model based on the nature of TMW that can rapidly and accurately predict the percentage values of metal and metalloid element occurrence forms in TMW. This method can minimize testing time requirements and help to assess TMW pollution risks, as well as further promote safe TMW management and recycling.

Evaluation of rheological and mechanical performance of gangue-based cemented backfill material: a novel hybrid machine learning approach

Waste discharge and surface damage are the unavoidable consequences of coal mining. However, filling waste into goaf can help reuse waste materials and protect the surface environment. In this paper, it is proposed to fill coal mine goaf with gangue-based cemented backfill material (GCBM), while the rheological and mechanical performances of GCBM influence the filling effect. A method that combines laboratory experiments and machine learning is proposed to predict the GCBM performance. The correlation and significance of eleven factors that affect GCBM are analyzed using random forest method, and the nonlinear effects of the main factors on the slump and uniaxial compressive strength (UCS) are analyzed. The optimization algorithm is improved, and the improved algorithm is combined with a support vector machine to build a hybrid model. The hybrid model is systematically verified and analyzed using predictions and convergence performances. The results demonstrate that the R² of the predicted and measured values is 0.93 and the root mean square error is 0.1912, indicating that the improved hybrid model can effectively predict the slump and UCS and can promote sustainable waste use.