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Accurate prediction of future electrical power demands greatly facilitates the task of power generation reliably and economically. In this paper, the new soft computing technologies, namely neuro-fuzzy techniques are adopted to improve precision of medium to long-term load forecasting. Performance of the proposed methodology is verified using simulations of some data pertaining to the Riyadh power system. This approach is compared with time series and neural networks design methods. It is demonstrated that the proposed methodology surpasses other methods by producing very accurate peak load forecasts. Keywords: Fuzzy logic, Neural networks, Neuro-fuzzy, Load forecasting

Abstract A novel methodology for extracting the optimal parameters of PV module with high efficiency InGaP/InGaAs/Ge triple-junction solar cells (TJSCc) is developed. Such methodology is based on recent meta-heuristic approach of Water Cycle Algorithm (WCA). The TJSC model is represented by single diode model and then a constrained objective function has been derived to be used in the optimization process of optimal parameter estimation. The analysis has been carried out based on high efficiency InGaP/InGaAs/Ge TJSC. The performance of WCA is evaluated and tested for TJSC based PV module operated with various irradiances and temperatures. Finally, a comparison between the proposed methodology and experimental data is carried out. Furthermore; other algorithms are programmed and applied to solve the problem under study like grey wolf optimizer (GWO), antlion optimizer (ALO), mine blast algorithm (MBA), harmony search algorithm (HSA) and the obtained results via these approaches are compared with those obtained by the proposed WCA. The results reveal the validity and superiority of WCA in extracting the optimal parameters of the TJSC based PV module.

Abstract The growing concerns of global warming and depleting oil/gas reserves have made it inevitable to seek energy from renewable energy resources. Many nations are embarking on introduction of clean/renewable solar energy for displacement of oil-produced energy. Moreover, solar photovoltaic (PV)–diesel hybrid power generation system technology is an emerging energy option since it promises great deal of challenges and opportunities for developed and developing countries. The Kingdom of Saudi Arabia (K.S.A) being enriched with higher level of solar radiation, is a prospective candidate for deployment of solar PV systems. Literature indicates that commercial/residential buildings in K.S.A. consume about 10–45% of the total electric energy generated. The aim of this study is to analyze long-term solar radiation data of Dhahran (East-Coast, K.S.A.) to assess the techno-economic feasibility of utilizing hybrid PV–diesel–battery power systems to meet the load of a typical residential building (with annual electrical energy demand of 35,120 kWh). The monthly average daily solar global radiation ranges from 3.61 to 7.96 kwh/m 2 . National Renewable Energy Laboratory's (NREL) Hybrid Optimization Model for Electric Renewable (HOMER) software has been employed to carry out the present study. The simulation results indicate that for a hybrid system composed of 4 kWp PV system together with 10 kW diesel system and a battery storage of 3 h of autonomy (equivalent to 3 h of average load), the PV penetration is 22%. The cost of generating energy (COE, US$/kWh) from the above hybrid system has been found to be 0.179 $/kWh (assuming diesel fuel price of 0.1$/l). The study exhibits that for a given hybrid configuration, the operational hours of diesel generators decrease with increase in PV capacity. The investigation also examines the effect of PV/battery penetration on COE, operational hours of diesel gensets for a given hybrid system. Concurrently, attention is focussed on un-met load, excess electricity generation, fuel savings and reduction in carbon emissions (for different scenarios such as PV–diesel without storage, PV–diesel with storage, as compared to diesel-only situation), COE of different hybrid systems, cost of PV–diesel–battery systems, etc.

Pathogens represent a significant threat to human health leading to the emergence of strategies designed to help manage their negative impact. We examined how spiritual beliefs developed to explain and predict the devastating effects of pathogens and spread of infectious disease. Analysis of existing data in studies 1 and 2 suggests that moral vitalism (beliefs about spiritual forces of evil) is higher in geographical regions characterized by historical higher levels of pathogens. Furthermore, drawing on a sample of 3140 participants from 28 countries in study 3, we found that historical higher levels of pathogens were associated with stronger endorsement of moral vitalistic beliefs. Furthermore, endorsement of moral vitalistic beliefs statistically mediated the previously reported relationship between pathogen prevalence and conservative ideologies, suggesting these beliefs reinforce behavioural strategies which function to prevent infection. We conclude that moral vitalism may be adaptive: by emphasizing concerns over contagion, it provided an explanatory model that enabled human groups to reduce rates of contagious disease.

This study addresses the issue of alternative pathways for breeding plutonium in a 900 MWe three loop thermal pressurized water reactor (PWR), either fueled with uranium fuel (3.5% U-235) or with mixed fuel (20% MOX). During the operation of a nuclear reactor the in-core neutron flux and the ex-core neutron flux are monitored with flux detectors. At the places where those detectors operate, the guide thimbles and the vessel wall, respectively, the neutron flux can be used to irradiate material samples. This paper investigates whether it would be possible to produce plutonium by breeding it at the walls of a PWR vessel and/or in the guide thimbles. The neutron flux in the reactor and the corresponding multi-group spectra are estimated with Monte Carlo simulations for different positions at the vessel wall of a PWR operating with either UO2 or MOX. Then the irradiation of fresh uranium samples at the vessel wall and in the guide thimbles are calculated and the isotopic composition of the irradiated samples are determined. The minimum irradiation period and the necessary minimum amount of fresh uranium to breed different grades of plutonium are derived.

Abstract A Gas–Solid Vortex Reactor (GSVR), in literature also referred to as Rotating Fluidized Bed in Static Geometry (RFB-SG), is a promising reactor type for Process Intensification (PI) with respect to reactor volume reduction. Although replacing gravitational by centrifugal force has been considered since the seventies, the hydrodynamics of the reactor flow and the bed behavior remain largely unknown. In the present work experiments have been carried out in a cold flow GSVR with diameter of 0.54 m, length of 0.1 m and 36 inlet slots of 2 mm. Gas injection velocities of 55–111 m/s and particles with densities of 950–1800 kg/m 3 and diameters of 1–2 mm have been applied. Depending on solids density, particle diameter and gas injection velocity the bed behavior can be considered as stable, semi-stable or unstable. For semi-stable and stable flow regimes the effect of the above process conditions on the maximum solids capacity was investigated. With increasing solids density, the bed stability considerably decreases, while the maximum solids capacity increases. By increasing the gas injection velocity, both the bed stability and the maximum solids capacity increase. Once bed stability is accomplished, a further increase of the gas injection velocity does not affect the maximum solids capacity. With increasing particle diameter, the bed stability decreases while the maximum solids capacity increases.

Agricultural waste-based heterogeneous catalysts are emerging as efficient and green catalysts. The present study explored the agricultural waste-based heterogeneous catalyst utilized in the production of biodiesel. The plant waste is composed of organic compounds and various metals which, on combustion, produces ashes that mainly consist of various metal carbonates and oxides. The most commonly employed approach for the solid catalyst preparation from plant materials is the calcination process, and it is performed at temperatures ranging from 300 to 1200°C. It is known that the temperature employed for calcination plays a vital role in the composition and development of the morphology of the catalyst. The variation in alkalinity, porosity, and, accordingly, the catalytic activity of the catalyst is significantly influenced by the calcination temperature. It was found that the potassium present in the form of oxide and carbonate as the main constituent in such catalysts played a significant role in delivering catalytic efficacy. Therefore, a number of agricultural waste-based catalysts were reported as efficient catalysts. The selection of the catalyst may be one of the important issues for application in large-scale biodiesel production. Thus, the present study was undertaken for the preparation of a rank list among the reported catalysts by following the VIKOR (Višekriterijumsko Kompromisno Rangiranje) multicriterion decision-making approach. In this work, the ranking study was performed considering the reported optimum reaction conditions (ORCs) of biodiesel synthesis reactions. The study was conducted strictly on the basis of the parameters, viz., catalyst concentration ( C 1 ), MTOR ( C 2 ), reaction temperature ( C 3 ), reaction time ( C 4 ), and biodiesel yield ( C 5 ). The parameters are considered good if C 1 , C 2 , C 3 , and C 4 are low or minimum and if C 5 is high or maximum. The catalyst prepared from plantain peel showed the best performance and ranked as the first one followed by Musa paradisiaca peel and cocoa pod husk catalysts which are ranked second. Thus, the VIKOR method can be useful for comparison and ranking purposes if there are a large number of data, and this may be expanded for thorough study by considering more criteria which may give more fruitful results.