Million cubic meters from abandoned mines worldwide could be used as subsurface reservoirs for large scale energy storage systems, such as adiabatic compressed air energy storage (A-CAES). In this paper, analytical and three-dimensional CFD numerical models have been conducted to analyze the thermodynamic performance of the A-CAES reservoirs in abandoned mines during air charging and discharging processes. Unlike other research works, in which the heat transfer coefficient is considered constant during the operation time, in the present investigation a correlation based on both unsteady Reynolds and Rayleigh numbers is employed for the heat transfer coefficient in this type of application. A tunnel with a 35 cm thick concrete lining, 200 m³ of useful volume and typical operating pressures from 5 to 8 MPa were considered. Fiber-reinforced plastic (FRP) and steel were employed as sealing layers in the simulations around the fluid. Finally, the model also considers a 2.5 m thick sandstone rock mass around the concrete lining. The results obtained show significant heat flux between the pressurized air and the sealing layer and between the sealing layer and concrete lining. However, no temperature fluctuation was observed in the rock mass. The air temperature fluctuations are reduced when steel sealing layer is employed. The thermal energy balance through the sealing layer for 30 cycles, considering air mass flow rates of 0.22 kg s⁻¹ (charge) and −0.45 kg s⁻¹ (discharge), reached 1056 and 907 kWh for FRP and steel, respectively. In general, good agreements between analytical and numerical simulations were obtained.

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

An SO2 emission episode at coal-fired power station occurs when the series of bihourly average of SO2 concentration, taken at 5-minute intervals, is greater than a specific value. Advance prediction of these episodes of pollution is very important for companies generating electricity by burning coal since it allows them to take appropriate preventive measures. In order to forecast SO2 pollution episodes, three different methods were tested: Elman neural networks, autoregressive integrated moving average (ARIMA) models, and a hybrid method combining both. The three methods were applied to a time series of SO2 concentrations registered in a control station in the vicinity of a coal-fired power station. The results obtained showed a better performance of the hybrid method over the Elman networks and the ARIMA models. The best prediction was obtained 115 minutes in advance by the hybrid model.

The drill-and-blast method is widely used for the excavation of hard rock tunnels. Toxic gases such as carbon monoxide and nitrogen oxides are released immediately after blasting by the detonation of explosives. To provide a safe working environment, the concentration of noxious gases must be reduced below the threshold limit value according to health and safety regulations. In this paper, one-dimensional mathematical models and three-dimensional CFD numerical simulations were conducted to analyze the concentration, propagation and dilution of the blasting fumes under different operating conditions. Forced, exhaust and mixed ventilation modes were compared to determine the safe re-entry times after blasting in a 200 m-long tunnel excavated using the top-heading-and-benching method. Based on the numerical simulations, carbon monoxide was the most critical gas, as it required a longer ventilation time to reduce its concentration below the threshold limit value. The safe re-entry time reached 480 s under the typical forced ventilation mode, but was reduced to 155 s when a mixed ventilation system was used after blasting, reducing the operating costs. The reduction of the re-entry time represents a significant improvement in the excavation cycle. In addition, the results obtained show that 1D models can be used to preliminary analyze the migration of toxic gases. However, to reliably determine the safe re-entry times, 3D numerical models should be developed. Finally, to verify the accuracy of the CFD results, field measurements were carried out in a railway tunnel using gas sensors. In general, good agreements were obtained between the 3D numerical simulations and the measured values.