In terms of energy production, combining conventional and renewable energy sources prove to be more sustainable and cost-effective. Nevertheless, efficient planning and designing of such systems are extremely complex due to the intermittency of renewable sources. Many existing studies fail to capture the stochasticity and/or avoid detailed reliability analysis. This research proposes a practical stochastic multi-objective optimization tool for optimally laying out and sizing the components of a grid-linked system to optimize system power at a low cost. A comparative analysis of four state-of-the-art algorithms using the hypervolume measure, execution time, and nonparametric statistical analysis revealed that the nondominated sorting genetic algorithm III (NSGA-III) was more promising, despite its significantly longer execution time. According to the NSGA-III calculations, given solar irradiance and energy profiles, the household would need to install a 5.5 (kWh) solar panel tilted at 26.3° and orientated at 0.52° to produce 65.6 (kWh) of power. The best battery size needed to store enough excess power to improve reliability was 2.3 (kWh). The cost for the design was $73520. In comparison, the stochastic technique allows for the construction of a grid-linked system that is far more cost-effective and reliable.
We present a new way of constructing a fractional-based convolution mask with an application to image edge analysis. The mask was constructed based on the Riemann-Liouville fractional derivative which is a special form of the Srivastava-Owa operator. This operator is generally known to be robust in solving a range of differential equations due to its inherent property of memory effect. However, its application in constructing a convolution mask can be devastating if not carefully constructed. In this paper, we show another effective way of constructing a fractional-based convolution mask that is able to find edges in detail quite significantly. The resulting mask can trap both local discontinuities in intensity and its derivatives as well as locating Dirac edges. The experiments conducted on the mask were done using some selected well known synthetic and Medical images with realistic geometry. Using visual perception and performing both mean square error and peak signal-to-noise ratios analysis, the method demonstrated significant advantages over other known methods.
Diffusive transport in porous media is a complex process in multi-scaled fractured media modeling. This paper presents a diffusive transport model for non-Dacian flow in a naturally fractured reservoir with triple porosity and permeability. To address the non-Darcian flow behavior associated with fluid transport in fractured porous media, the Darcy/Forcheimer equation was used. A point-diffusive equation was obtained from mass conservation and the Darcy–Forcheimer momentum equation; this is used together with interface conditions to incorporate the microscopic properties of the domain. Subsequently, the resulting equation was spatially smoothed to obtain an effective macroscopic average model. The macroscopic model obtained, unlike the existing models, has a cross-diffusive term for mass transport by induced fluxes and a mass transfer term accounting for mass transfer between the matrix and the surrounding fractures via the interface. The numerical simulation displayed a horizontal-linear flow behavior in the fractured network instead of a radial flow in the matrix. The results further suggest that despite the fractures aiding in fluid transport, they enhance fluid production in the reservoir compared to the matrix.
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