Implementation Of Hydrogen Economy Research Articles

Nanointerface Engineering of Metal Hydrides for Advanced Hydrogen Storage

With global efforts to relieve the formidable impact of climate change, hydrogen is considered a viable replacement for fossil fuels without intermittency concerns of other renewable sources. Hydrogen storage plays a pivotal role in the implementation of hydrogen economy, coupling hydrogen production with fuel cell technologies. Storing hydrogen in the form of solid-state hydride materials has been studied as a future hydrogen storage technology for enabling a safe, energy-efficient, and high-energy-density system. However, hostile thermodynamic and kinetic properties of each hydride material result in insufficient hydrogen storage performance for practical applications, such as sluggish hydrogen absorption or desorption, high dehydrogenation temperatures, and sometimes limited reversibility; thus, these kinetic and thermodynamic characteristics need to be thoroughly understood depending on each hydride material. Among various strategies, nanostructuring has been regarded as a general approach to tackling such limitations regarding thermodynamic and kinetic characteristics of hydride materials. In particular, the formation of nanosized hydrides within a nanostructured scaffold─also known as nanoconfinement─is of great potential for advanced hydrogen storage because it can additionally leverage host–guest interactions at the nanointerfaces of hydride materials and scaffolds. In this context, the active tuning of such nanointerfaces brings about additional thermodynamic or kinetic changes in hydrogen sorption reactions compared to the unmodified nanoconfined hydride composites, holding great promise for tailored strategies for each metal hydride. In this Perspective, we summarize the major thermodynamic and kinetic barriers of each metal hydride and highlight the recent progress in overcoming such limits, mainly focusing on nanointerface engineering in nanoconfined metal hydrides. Further, we provide our insight and current challenges in understanding the underlying mechanisms of the interaction at the nanointerface, whereby the noticeable technological leaps can be emulated in practical systems.

Design and implementation of hydrogen economy using artificial neural network on field programmable gate array

In this study, economic analysis of the hydrogen generation and liquefaction system has been modeled using Multi-Layer Feed-Forward Artificial Neural Network (MLFFANN) and implemented on Field Programmable Gate Array (FPGA). Firstly, the 100X6 data set has been created to be used in the ANN-based modeling of the system using the Engineering Equation Solver (EES) program. This data set has been divided into two data sets as 80X6 for training and 20X6 for testing. The structure of the ANN-based economic analysis of hydrogen generation and liquefaction has been composed of 3 neurons in the input layer, ten neurons in the hidden layer, and three neurons in the output layer. Elliott-2-based TanSig transfer function and Purelin transfer function have been used in the neurons of the hidden layer and the output layer, respectively. Then, the ANN-model has been trained and tested using the Matlab program. The MSE values, 1.40x10E-7 and 2.07x10E-5, have been obtained as the results of the training phase and test phase of the ANN-based system, respectively. After getting fruitful results from training and testing phases, the economic analyses of hydrogen generation and liquation systems have been modeled in VHDL using bias and weight values located in the constructed ANN-based system using Matlab. The modeling has been performed in the Xilinx ISE Design Tools program using a 32-bit IEEE-754-1985 floating-point number standard. Then, the modeled ANN-based economic analysis of the hydrogen generation and liquation system has been implemented on the Xilinx Virtex-7 FPGA chip by performing the Place&Route process. The maximum operating frequency of the ANN-based hydrogen generation and liquefaction economy system implemented on FPGA has been obtained as 281.702 MHz using Xilinx ISE Design Tools.