The heap leaching of minerals is one of the more commonly used processes in the mining industry. This process has been modeled by many authors. However, the validation, verification, and implementation of these models are difficult since there is uncertainty about the operating conditions and the leaching model parameters. This work uses the uncertainty quantification, based on uncertainty and sensitivity analysis, for studying the model strength against uncertainties in heap leaching. The uncertainty analysis (UA) is used to quantify the effect of the magnitude of the uncertainties of the input variables on the recovery of heap leaching. Global sensitivity analysis (GSA) is used to study the nature of connections between the recovery and input variables of the leaching model. In addition, GSA facilitates the detection of whether a leaching model is over-parameterized. The information obtained allows studying some applications of the kinetic model. The Mellado et al. kinetic model is used as an example. The UA results indicate that the kinetic model can estimate the recovery behavior considering the full range of uncertainties of input variables. The GSA indicates that the kinetic model is over-parameterized on the uncertainties range considered; this conclusion contradicts the results when the local sensitivity analysis is used. However, the model shows a good correlation between the results of GSA and the kinetic behavior of heap leaching. In addition, the kinetic model presents versatility because it allows the determination of operating regions for heap leaching.

Response surface methodology (RSM) is one of the most effective tools for optimizing processes, and it has been used in conjunction with the Analysis of Variance (ANOVA) test to establish the effect of input factors on output factors. However, when this methodology is used in mineral flotation, its polynomial model usually performs poorly. An alternative is to use artificial neural networks (ANNs) in such situations. Within this context, the ANOVA test is not the best option for these model types; moreover, it requires statistical assumptions that are difficult to satisfy in flotation. This work proposes replacing the polynomial model of the RSM with ANNs and the Sobol methods to determine the influential input factors instead of the ANOVA test. This proposal is applied to two porphyry copper ores with a high content of pyrite, clay, and dilution media. In addition, this study shows how other computational intelligence techniques, such as swarm intelligence, can be incorporated into this type of problem to improve the learning process of ANNs. The results gave an adjustment of over 0.98 for R² using ANNs, in comparison to values of around 0.5 when the polynomial model of RSM was utilized. On the other hand, the application of Global Sensitivity Analysis (GSA) identified the aeration rate and P₈₀ size as the most influential variables in copper recovery under the conditions studied. Additionally, we identified significant interactions that affect the recovery of copper, with the interactions between the aeration rate, frother concentration, and P₈₀ size being the most important.

Open pit mining is based on dynamic operations within the open pit, with the haulage operation being the most decisive in terms of the cost and the overall productivity of the operation. A vital aspect for the success of the haulage operation are the conditions of the roads, which change over time due to wear and tear. The present work aims to model and to evaluate the effect of temporal deterioration affecting mining roads in an open pit mining operation through rolling resistance, using the discrete event simulation (SED) tool to evaluate two key aspects of the operation: productivity and fuel consumption. Additionally, to reverse road deterioration, the assignment of a grader with a variable frequency of use will be modeled through a parallel model. The case study is a 5 km road with a slope of 9.5%, a fleet of 10 trucks, and their capacities of 300 tons, loaded in a 24 h simulation model. The results show that not considering road deterioration can mean an overestimation of productivity of up to 600 Ton/hr, and an overconsumption of 78%. The application of a motor grader could decrease the negative effects of roads deterioration, which depends on the frequency of its assignment. The results show that for the rate of productivity and fuel consumption for every hour of frequency that the motor grader does not do road maintenance, the production loss is 600 tons, and the fuel consumption increases by approximately 1800 L in a day.

The solar electroflotation (EF) processes using saline electrolytes are today one of the great challenges for the development of electrochemical devices, due to the corrosion problems that are generated during the operation by being in permanent contact with Cl⁻ ions. This manuscript discloses the corrosion behavior of titanium electrodes using a superposition model based on mixed potential theory and the evaluation of the superficial performance of the Ti electrodes operated to 4 V/SHE solar electroflotation in contact with a solution of 0.5 M NaCl. Additionally provided is an electrochemical analysis of Ti electrodes regarding HER, ORR, OER, and CER that occur during the solar saline EF process. The non-linear superposition model by mixed potential theory gives electrochemical and corrosion parameters that complement the information published in scientific journals, the corrosion current density and corrosion potential in these conditions is 0.069 A/m² and −7.27 mV, respectively. The formation of TiO₂ and TiOCl on the anode electrode was visualized, resulting in a reduction of its weight loss of the anode electrode.

The presence of ultrafine clay particles that are difficult to remove by conventional filtration creates many operational problems in mining processing systems. In this work, the removal of clay suspensions has been investigated using an electroflotation (EF) process with titanium electrodes. The results show that EF is a viable and novel alternative for removing ultrafine particles of kaolinite-type clay present in sedimentation tank overflows with low salt concentrations (<0.1 mol/L) in copper mining facilities based on the saline water splitting concept. Maximum suspended solid removal values of 91.4 and 83.2% in NaCl and KCl solutions, respectively, were obtained under the experimental conditions of the constant applied potential of 20 V/SHE, salinity concentration of 0.1 mol/L, and electroflotation time of 10 and 20 min in NaCl and KCl solutions, respectively. Furthermore, the visual evidence of particle aggregation by flocculation during the experiments indicates a synergy between EF and electrocoagulation (EC) that enhances the removal of ultrafine particles of kaolinite.