Proton Exchange Membrane Fuel Cells (PEMFC) is considered a propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and an in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, recent meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Marine Predators Algorithm (MPA) and Political Optimizer (PO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W, SR-12PEM 500 W, and 250 W stack under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with those obtained in the literature. Moreover, the I/V curves obtained by the application of MPA and PO showed a clear matching with datasheet curves for all the studied cases. Statistical analysis has been performed to evaluate the robustness of the MPA and PO techniques. Finally, the PEMFC model based on the MPA technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed. The obtained results confirmed the superiority and reliability of the applied approach of the MPA algorithm. The results prove that the MPA algorithm has a superior performance based on its reliability. INDEX TERMS Fuel cell modelling, Parameter estimation, metaheuristic algorithms.

Seals prepared from acrylonitrile-butadiene rubber (NBR) are primarily used in nuclear services. Nevertheless, at relatively high ionizing radiation doses, NBR seal materials may undergo radiation-induced degradation processes, leading to adverse effects on the sealing ability life. Herein, to strengthen the functional characteristics of NBR seals against radiation, graphene oxide (GO) nanoparticles were prepared and characterized by transmission electron microscopy, X-ray diffraction (XRD), Fourier transform infrared (FTIR), and ultraviolet/visible spectroscopies. Various NBR/GO composites fabricated with different ratios of GO nanoparticles and in the presence or absence of carbon black (CB) were investigated via cross-linking density, scanning electron microscopy, XRD, FTIR, and mechanical and thermal stability analyses. The synergistic effect of the simultaneous presence of GO and CB on the NBR seal sensitization to gamma radiation up to a dose of 1 MGy was studied. The physicomechanical properties were enhanced by adding GO nanosheets up to 3 phr and by incorporating 35 phr of a CB with GO until 5 phr. Further, the application of γ-irradiation resulted in an overall enhancement in the mechanical, physical, and thermal stability of the prepared composites up to 0.5 and 1 MGy with GO nanosheets in the absence or presence of CB particles, respectively.The mechanical measurements indicated significant increments by loading with GO nanosheets in the absence and presence of CB as well as by irradiation. The tensile strength elevated up to about 121%, 336%, and 366% by adding 3 phr GO, 3 GO:35 CB phr, and 5 GO:35 CB phr, respectively. K E Y W O R D Sacrylonitrile-butadiene rubber seal, carbon black, gamma radiation, graphene oxide nanoparticles, mechanical and thermal characteristics, synergism

Nanocomposite polymer electrolytes (NCPE) were prepared using nano polyethylene oxide PEO doped with Magnesium (Mg) salts. Gamma irradiation was utilized to improve the PEO-Mg salts particle sizes. Consequently, Magnesium Oxide (MgO) nanoparticles were prepared by green synthesis and incorporated into PEO-Mg salts to improve their properties toward magnesium battery electrolyte applications. The prepared samples were examined before and after exposures to the radiation doses. Dynamic light scattering (DLS) indicated the particles size of the synthesized nano polymer-Mg salts and MgO nanoparticles. Fourier transform infra-red (FTIR) spectroscopic measurements, transmission electron microscopy (TEM), electrical conductivity, electrochemical properties, and thermal stability of the samples were determined. FTIR indicated the interaction between PEO with Mg salts and MgO nanoparticles which confirmed the structure. The TEM results showed a spherical nanoparticles of MgO and a good dispersion of MgO in PEO matrix. It was found that the irradiation dose 70 kGy gave the best results for the nano polymer-Mg salts (13 nm). The electrical conductivity (σ) evaluated for NCPE, was more than three orders of magnitude of pure PEO. The liquid NCPE of 20 mL MgO NPs at 100 kGy exhibited a maximum conductivity of 3.63 × 10 -3 Scm −1 at room temperature. The increase in temperature caused a slight effect on conductivity, 4.85 × 10 -3 Scm −1 at temperature 250 C, at the same concentration. While un-irradiated sample of 30 mL MgO NPs (σ) reached to 3.8 × 10 −3 Scm −1 then became 5.03 × 10 −3 Scm −1 by increasing temperature. From the cyclic voltammetry results, the polymer electrolytes containing MgO filler, 20 and 30 mL, for irradiated and un-irradiated samples, respectively exhibited wider electrochemical stability window than the others due to the appearance of Mg deposition/desolution peak in CV curve showed that magnesium effectively migrating through electrolytes. Thermogravimetric analysis (TGA) was enhanced by adding Mg salts electrolyte and also MgO nanoparticles to PEO. J. VINYL ADDIT. TECHNOL., 25:243-254, 2019.

Scientists have been paying more attention to the shortage of water and energy sources all over the world, especially in the Middle East and North Africa (MENA). In this article, a microgrid configuration of a photovoltaic (PV) plant with fuel cell (FC) and battery storage systems has been optimally designed. A real case study in Egypt in Dobaa region of supplying safety loads at a nuclear power plant during emergency cases is considered, where the load characteristics and the location data have been taken into consideration. Recently, many optimization algorithms have been developed by researchers, and these algorithms differ from one another in their performance and effectiveness. On the other hand, there are recent optimization algorithms that were not used to solve the problem of microgrids design in order to evaluate their performance and effectiveness. Optimization algorithms of equilibrium optimizer (EQ), bat optimization (BAT), and black-hole-based optimization (BHB) algorithms have been applied and compared in this paper. The optimization algorithms are individually used to optimize and size the energy systems to minimize the cost. The energy systems have been modeled and evaluated using MATLAB.

This paper proposes a new risk assessment methodology using fuzzy logic model based on the risk matrix information and applications to the nuclear facilities. The structure of the fuzzy Inference System (FIS) is formed in fuzzifier, knowledge base and defuzzifier. Applications of the fuzzy system involves analyzing and managing the risk in nuclear reactors based on the classification of the events information. The input and output of the fuzzy system are simulated in crisp value. The proposed fuzzy model; and operator experiences were the devices for making the rules and inherent connection between variables in fuzzy model. Fuzzy logic is one of the intelligence systems and it has wide range applications in fault analysis, event classification, accident analysis, safety and risk assessment. The structure of risk matrix reflects the shape of the membership functions and the If-Then rules of the fuzzy model design. The risk matrix is simulated in the fuzzy approach to make it easier as a model based on If-Then rules. Simulation results illustrated that fuzzy logic system gives many advantages for risk assessment such as the dynamic modeling in If-Then rules.