Hydrogen is a flexible energy carrier and storage medium and can be generated by electrolysis of water. In this research, hydrogen generation is maximized by optimizing the optimal sizing and operating condition of an electrolyzer directly connected to a PV module. The method presented here is based on Particle swarm optimization algorithm (PSO). The hydrogen, in this study, was produced using a proton exchange membrane (PEM) electrolyzer. The required power was supplied by a photovoltaic module rated at 80 watt. In order to optimize Hydrogen generation, the cell number of the electrolyser and its activity must be 9 and 3, respectively. As a result, it is possible to closely match the electrolyzer polarization curve to the curve connecting PV system’s maximum power points at different irradiation levels. PSO is a novel method in optimization inspiring from observation of bird flocking and fish schooling. Comparing to other optimization method, not only PSO is more efficient and require lower functions of evaluations, but it leads to better results, as well.

In this research, new software package for neutronic calculations, especially kinetic parameters of PWR reactors, has been developed. The program used to link the WIMS-D5, BORGES and CITVAP nuclear codes has been written in Visual C# programming language. This software was used for calculation of kinetic parameters of WER-1000 and NOK Beznau reactors. The ratios ( β eff ) i /( β eff ) core of parameters, which are an important input data for the reactivity accident analysis, were also calculated. The results were compared with final safety analysis report (FSAR) and published documents.

In this paper design and evaluation of an adaptive critic-based neurofuzzy controller for the stabilizing periodic orbits of chaotic systems has been presented in detail. The main superiority of the proposed controller over previous analogous fuzzy logic controller design approaches, e.g., genetic fuzzy logic controller, is its online tuning characteristic and remarkable reduced amount of computations used for parameter adaptation, which makes it desirable for real time applications. Considering the simplicity of this controller and its independence from the system model, this control method has the advantage of online learning and control, and can be applied to a large variety of systems. The proposed adaptive scheme is used for stabilizing the 2-π and 4-π periodic orbits of a Duffing system to investigate numerically the effectiveness of the method. Also the robustness of the proposed controller was examined using input noise and parameter uncertainty in the system model. Simulation results show that the proposed algorithm can be successfully used for chaos suppression when there is not any crisp mathematical model of the dynamical system.