Young’s modulus (E) is essential for predicting the behavior of materials under stress and plays an important role in the stability of surface and subsurface structures. E has a wide range of applications in mining, geology, civil engineering, etc.; for example, coal and metal mines, tunnels, foundations, slopes, bridges, buildings, drilling, etc. This study developed a novel machine learning regression model, namely an extreme gradient boosting (XGBoost) to predict the influences of four inputs such as uniaxial compressive strength in MPa; density in g/cm3; p-wave velocity (Vp) in m/s; and s-wave velocity in m/s on two outputs, namely static Young’s modulus (Es) in GPa; and dynamic Young’s modulus (Ed) in GPa. Using a series of basic statistical analysis tools, the accompanying strengths of each input and each output were systematically examined to classify the most prevailing and significant input parameters. Then, two other models i.e., multiple linear regression (MLR) and artificial neural network (ANN) were employed to predict Es and Ed. Next, multiple linear regression and ANN were compared with XGBoost. The original dataset was allocated as 70% for the training stage and 30% for the testing stage for each model. To improve the performance of the developed models, an iterative 10-fold cross-validation method was used. Therefore, based on the results XGBoost model has revealed the best performance with high accuracy (Es: correlation coefficient (R2) = 0.998; Ed: R2 = 0.999 in the training stage; Es: R2 = 0.997; Ed: R2 = 0.999 in the testing stage), root mean square error (RMSE) (Es: RMSE = 0.0652; Ed: RMSE = 0.0062 in the training stage; Es: RMSE = 0.071; Ed: RMSE = 0.027 in the testing stage), RMSE-standard deviation ratio (RSR) index value (Es: RSR = 0.00238; Ed: RSR = 0.00023 in the training stage; Es: RSR = 0.00304; Ed: RSR = 0.001 in the testing stage) and variance accounts for (VAF) (Es: VAF = 99.71; Ed: VAF = 99.99 in the training stage; Es: VAF = 99.83; Ed: VAF = 99.94 in the testing stage) compared to the other developed models in this study. Using a novel machine learning approach, this study was able to deliver substitute elucidations for predicting Es and Ed parameters with suitable accuracy and runtime.

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Flotation is a common mineral processing method used to upgrade copper sulfide ores; in this method, copper sulfide mineral particles are concentrated in froth, and associated gangue minerals are separated as tailings. However, a significant amount of copper is lost into tailings during the processing; therefore, tailings can be considered secondary resources or future deposits of copper. Particle–bubble collision efficiency and particle–bubble aggregate stability determines the recovery of target particles; this attachment efficiency plays a vital role in the selectivity process. The presence of fine particles in the flotation circuit is because of excessive grinding, which is to achieve a higher degree of liberation. Complex sulfide ores of markedly low grade further necessitate excessive grinding to achieve the maximum degree of liberation. In the flotation process, fine particles due to their small mass and momentum are unable to collide with rising bubbles, and their rate of flotation is very slow, further lowering the recovery of target minerals. This collision efficiency mainly depends on the particle–bubble size ratio and the concentration of particles present in the pulp. To overcome this problem and to maintain a favorable particle–bubble size ratio, different techniques have been employed by researchers to enhance particle–bubble collision efficiency either by increasing particle size or by decreasing bubble size. In this article, the mechanism of tailing loss is discussed in detail. In addition, flotation methods for fine particles recovery such as microbubble flotation, column flotation, nanobubble flotation, polymer flocculation, shear flocculation, oil agglomeration, and carrier flotation are reviewed, and their applications and limitations are discussed in detail.