The main objective of the research was to delimit the final pit using the maximum flow pseudoflow mathematical model in an open-pit mine, 2023. The methodology employed was non-experimental, descriptive, and applicative design. The population consisted of all block models from open-pit mines, and the sample consisted of the block model from one open-pit mine. To validate the hypothesis, quality control was used with a significance level of 5%. The critical value obtained was 838.72, and the F statistic was 5.14. The results revealed that the block model consisted of 480,000 blocks with dimensions of 10x10x10 m, and the copper grade ranged from 0% to 1.41%. The necessary parameters for the delimitation of the final pit were determined, including a slope angle of 45°. In financial terms, values such as the base copper price of 3.9 US$/lb, smelting cost of 0.4 US$/lb, mining cost of 2.3 US...

The objective of this study is to predict the quantity of ANFO required for bench blasting in an open pit mine in Peru, through the application of advanced machine learning techniques. Six models were selected: Artificial Neural Networks (ANNMLP), Random Forests (RF), Support Vector Machines for Regression (SVR), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Bayesian Regression (BR), due to their ability to handle complex multidimensional data and their success in similar applications, such as rock fragmentation prediction. The methodology included the collection of data from 208 drill holes, which were divided into training (70%), validation (15%), and testing (15%) sets. The models were evaluated using RMSE, MSE, MAE, and R2. The KNN model showed the best performance, with an R2 of 0.84, RMSE of 2.37, MSE of 5.60, and MAE of 1.35, standing out in predictive accura...

Mine tailings represent a secondary resource of growing interest in the context of the energy transition and circular economy. This study evaluated the potential for recovering critical elements from Tailings Deposit I of the La Cienega site, Peru, through the integration of geochemical and mineralogical characterization tools and three-dimensional geostatistical modeling. A total of 68 samples were analyzed for cobalt (Co), nickel (Ni), lithium (Li), and phosphorus (P), and 10 samples for uranium (U) and tungsten (W). The results revealed a significant presence of these elements, with localized enrichment zones and distinct geochemical associations. Ni and Co exhibited spatial patterns consistent with their likely occurrence in residual sulfides, whereas Li, W, U, and P showed more heterogeneous distributions. Three-dimensional modeling performed using ordinary kriging and validated thr...