Injection of supercritical CO2 (scCO2) for the geologic storage of carbon dioxide will displace formation water, and the pore space adjacent to overlying caprocks could eventually be dominated by dry to water- saturated scCO2. Wet scCO2 is highly reactive and capable of carbonating and hydrating certain minerals, whereas anhydrous scCO2 can dehydrate water-containing minerals. Because these geochemical processes affect solid volume and thus porosity and permeability, they have the potential to affect the long-term integrity of the caprock seal. In this study, we investigate the swelling and shrinkage of an expandable clay found in caprock formations, montmorillonite (Ca-STx-1), when exposed to variable water-content scCO2 at 50°C and 90bar using a combination of in situ probes, including X-ray diffraction (XRD), in situ magic angle spinning nuclear magnetic resonance spectroscopy (MAS NMR), and in situ attenuated total reflection infrared spectroscopy (ATR-IR). We show that the extent of montmorillonite clay swelling/shrinkage is dependent not only on water hydration/dehydration, but also on CO2 intercalation reactions. Our results also suggest a competition between water and CO2 for interlayer residency where increasing concentrations of intercalated water lead to decreasing concentrations of intercalated CO2. Overall, this paper demonstrates the types of measurements required to develop fundamental knowledge that will enhance modeling efforts and reduce risks associated with subsurface storage of CO2

Photovoltaic energy production is an important factor for increasing the electricity supply. The ability to predict the electric power production (EPP) of a photovoltaic (PV) farm supports from the management process of the power grid to the trade in the energy market and much more. Also, by predicting the production of PV power (PVP), it is possible to monitor the lifetime of the solar cells that form the backbone of any solar PV system. As a critical result, sudden failures of the PV plant can be avoided. Using a long short-term memory recurrent neural network (LSTM-RNN) model, this work evaluates the prediction accuracy of two forecasting strategies: the recursive strategy and the non-recursive Multiple-Input and Multiple-Output, respectively. The dataset consists of 5-years in-filed production data measurements collected from the CETATEA photovoltaic power plant, a research site facility for renewable energies located in Cluj-Napoca, Romania. The high granularity of the electric power production dataset values recorded each 1 hour guarantees the overall prediction accuracy of the system. The impact of the dataset size, the number of previous observations, and the forecast horizon on the neural network prediction accuracy is evaluated for each strategy. The performance metrics used to evaluate the prediction accuracy are the root mean square error, the mean bias error, and the mean average error. The results analysis demonstrates the ability of the implemented machine learning models to predict electric power production, as well as their importance in the energy loss management process.