Metal nanoparticles (MNPs) are the main agents in heterogeneous catalysis. Hence, utilizing the effective physico-chemical methods to engage them to achieve the highest catalysts performance with well-controlled size, shape, and surface properties seems to be essential. The encapsulation of metal nanoparticles is a promising approach that enhances the catalytic activity of the materials. Not only the encapsulating structures can adjust the catalytic properties of metal nanoparticles, particularly selectivity, but also prevents them from agglomeration and sintering. In this chapter, the various encapsulating structures consist of yolk/core-shell and mesoporous structures, and encapsulating materials that are divided into three parts, including inorganic materials, metal–organic frameworks, and organic materials are presented.
In this study, optimal load sharing strategy for a stand-alone hybrid power generation system that consists of wind turbine, diesel generator and battery banks is presented. The diesel generator is used to complement the intermittent output of the wind source whereas the battery is used to compensate for part of the temporary peak demand, which the wind and diesel generator cannot meet thus avoiding oversizing of the diesel generator. To optimise the performance of the system, imperialist competitive algorithm (ICA), ant colony optimisation (ACO) and particle swarm optimisation (PSO) are used to optimal load sharing. These algorithms are used to select the best available energy source so that the system has the best performance.To verify the system performance simulation studies have been carried out using forecasted data (load demand and wind speed). Accordingly, ICA, ACO and PSO are used to train a three-layer feed forward neural network. This trained artificial neural network is applied to short-term wind speed and load demand forecasting on a specific day in the Qazvin. The results show that the proposed control methods can reduce fuel consumption and increase the battery lifetime and battery ability to respond to real-time load turbulences simultaneously.
This paper studies an efficient way to produce syngas from the methane couple reforming and partial methane oxidation by utilizing a catalytic plate reactor. Methane steam and dry reforming as endothermic reactions are coupled with partial methane oxidation as an exothermic reaction in a catalytic plate reactor, which is simulated using detailed reaction kinetics, mass, and energy balances. The impact of inlet temperature, composition, and velocities on the reforming and partial oxidation channels, and also the resulting methane conversions, is studied. In addition, the H2/CO ratio is evaluated for both endothermic and exothermic sides across varied feed ratios. Co-and counterflow arrangements are simulated for catalytic plate reactors, and their impact on temperature distribution and methane conversion is studied. The suitable plate dimensions, in particular, plate length, are computed during this simulation. Applying a metal plate, Co-and counter-flow arrangements are simulated for catalytic plate reactors, and their impact on temperature distribution and methane conversion is studied. During this simulation, the appropriate plate dimensions, particularly plate length, are determined. The use of a metal plate with a greater thermal conductivity allows for effective heat transmission between endothermic and exothermic channels, resulting in outstanding temperature distribution and slight temperature differences.
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