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.

Adsorbent density impact on gas storage capacities

In the literature, different approaches, terminologies, concepts and equations are used for calculating gas storage capacities. Very often, these approaches are not well defined, used and/or determined, giving rise to significant misconceptions. Even more, some of these approaches, very much associated with the type of adsorbent material used (e.g., porous carbons or new materials such as COFs and MOFs), impede a suitable comparison of their performances for gas storage applications. We review and present the set of equations used to assess the total storage capacity for which, contrarily to the absolute adsorption assessment, all its experimental variables can be determined experimentally without assumptions, ensuring the comparison of different porous storage materials for practical application. These material-based total storage capacities are calculated by taking into account the excess adsorption, the bulk density (ρbulk) and the true density (ρtrue) of the adsorbent. The impact of the material densities on the results are investigated for an exemplary hydrogen isotherm obtained at room temperature and up to 20 MPa. It turns out that the total storage capacity on a volumetric basis, which increases with both, ρbulk and ρtrue, is the most appropriate tool for comparing the performance of storage materials. However, the use of the total storage capacities on a gravimetric basis cannot be recommended, because low material bulk densities could lead to unrealistically high gravimetric values.

GPGPU implementation of growing neural gas: application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6× compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.

3D-based reconstruction using growing neural gas landmark: application to rapid prototyping in shoe last manufacturing

Customizing shoe manufacturing is one of the great challenges in the footwear industry. It is a production model change where design adopts not only the main role, but also the main bottleneck. It is therefore necessary to accelerate this process by improving the accuracy of current methods. Rapid prototyping techniques are based on the reuse of manufactured footwear lasts so that they can be modified with CAD systems leading rapidly to new shoe models. In this work, we present a shoe last fast reconstruction method that fits current design and manufacturing processes. The method is based on the scanning of shoe last obtaining sections and establishing a fixed number of landmarks onto those sections to reconstruct the shoe last 3D surface. Automated landmark extraction is accomplished through the use of the self-organizing network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates up to 12 times the surface reconstruction and filtering processes used by the current shoe last design software. The proposed method offers higher accuracy compared with methods with similar efficiency as voxel grid.

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.

Carbon-supported ionic liquids as innovative adsorbents for CO2 separation from synthetic flue-gas

Fixed-bed thermodynamic CO2 adsorption tests were performed in model flue-gas onto Filtrasorb 400 and Nuchar RGC30 activated carbons (AC) functionalized with [Hmim][BF4] and [Emim][Gly] ionic liquids (IL). A comparative analysis of the CO2 capture results and N2 porosity characterization data evidenced that the use of [Hmim][BF4], a physical solvent for carbon dioxide, ended up into a worsening of the parent AC capture performance, due to a dominating pore blocking effect at all the operating temperatures. Conversely, the less sterically-hindered and amino acid-based [Emim][Gly] IL was effective in increasing the AC capture capacity at 353 K under milder impregnation conditions, the beneficial effect being attributed to both its chemical affinity towards CO2 and low pore volume reduction. The findings derived in this work outline interesting perspectives for the application of amino acid-based IL supported onto activated carbons for CO2 separation under post-combustion conditions, and future research efforts should be focused on the search for AC characterized by optimal pore size distribution and surface properties for IL functionalization.

Applications for CO2-Activated Carbon Monoliths: I. Gas Storage

Carbon monoliths with high densities are studied as adsorbents for the storage of H2, CH4, and CO2 at ambient temperature and high pressures. The starting monolith A3 (produced by ATMI Co.) was activated under a CO2 flow at 1073 K, applying different activation times up to 48 h. Micropore volumes and apparent surface areas were deduced from N2 and CO2 adsorption isotherms at 77 K and 273 K, respectively. CO2 and CH4 isotherms were measured up to 3 MPa and H2 up to 20 MPa. The BET surface area of the starting monolith (941 m2/g) could be significantly increased up to 1586 m2/g, and the developed porosity is almost exclusively comprised of micropores <1 nm. Total storage amounts take into account the compressed gas in the void space of the material, in addition to the adsorbed gas. Remarkably, high total storage amounts are reached for CO2 (482 g/L), CH4 (123 g/L), and H2 (18 g/L). These values are much higher than for other sorbents with similar surface areas, due to the high density of the starting monolith and of the activated ones, for which the density decreases only slightly (from 1.0 g/cm3 to 0.8 g /cm3 upon CO2 activation). The findings reveal the suitability of high density activated carbon monoliths for gas storage application. Thus, the amounts of stored gas can be increased by more than a 70 % in the case of H2 at 20 MPa, almost 5.5 times in the case of CH4 at 3 MPa, and more than 7.5 times in the case of CO2 at 3 MPa when adsorbents are used for gas storage under the investigated conditions rather than simple compression. Furthermore, the obtained results have been recently confirmed by a scale-up study in which 2.64 kg of high density monolith adsorbent was filled a tank cylinder of 2.5 L (Carbon, 76, 2014, 123).