Gas Storage Scale-up at Room Temperature on High Density Carbon Materials

In relation to the current interest on gas storage demand for environmental applications (e.g., gas transportation, and carbon dioxide capture) and for energy purposes (e.g., methane and hydrogen), high pressure adsorption (physisorption) on highly porous sorbents has become an attractive option. Considering that for high pressure adsorption, the sorbent requires both, high porosity and high density, the present paper investigates gas storage enhancement on selected carbon adsorbents, both on a gravimetric and on a volumetric basis. Results on carbon dioxide, methane, and hydrogen adsorption at room temperature (i.e., supercritical and subcritical gases) are reported. From the obtained results, the importance of both parameters (porosity and density) of the adsorbents is confirmed. Hence, the densest of the different carbon materials used is selected to study a scale-up gas storage system, with a 2.5 l cylinder tank containing 2.64 kg of adsorbent. The scale-up results are in agreement with the laboratory scale ones and highlight the importance of the adsorbent density for volumetric storage performances, reaching, at 20 bar and at RT, 376 g l-1, 104 g l-1, and 2.4 g l-1 for CO2, CH4,and H2, respectively.

Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion With SAR and Temperature

In this study, a methodology based in a dynamical framework is proposed to incorporate additional sources of information to normalized difference vegetation index (NDVI) time series of agricultural observations for a phenological state estimation application. The proposed implementation is based on the particle filter (PF) scheme that is able to integrate multiple sources of data. Moreover, the dynamics-led design is able to conduct real-time (online) estimations, i.e., without requiring to wait until the end of the campaign. The evaluation of the algorithm is performed by estimating the phenological states over a set of rice fields in Seville (SW, Spain). A Landsat-5/7 NDVI series of images is complemented with two distinct sources of information: SAR images from the TerraSAR-X satellite and air temperature information from a ground-based station. An improvement in the overall estimation accuracy is obtained, especially when the time series of NDVI data is incomplete. Evaluations on the sensitivity to different development intervals and on the mitigation of discontinuities of the time series are also addressed in this work, demonstrating the benefits of this data fusion approach based on the dynamic systems.