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Abstract In-core nuclear fuel management is one of the most important concerns in the design of nuclear reactors. Two main goals in core fuel loading pattern design optimization are maximizing the core effective multiplication factor in order to extract the maximum energy, and keeping the local power peaking factor lower than a predetermined value to maintain the fuel integrity. Because of the numerous possible patterns of fuel assemblies in the reactor core, finding the best configuration is so important and challenging. Different techniques for optimization of fuel loading pattern in the reactor core have been introduced by now. In this study, a software is programmed in C# language to find an order of the fuel loading pattern of a VVER-1000 reactor core using the perturbation theory. Our optimization method is based on minimizing the radial power peaking factor. The optimization process launches by considering an initial loading pattern and the specifications of the fuel assemblies which are given as the input of the software. The results on a typical VVER-1000 reactor reveal that the method could reach to a pattern with an allowed radial power peaking factor and increases the cycle length 1.1 days, as well.

Abstract The power system needs to satisfy its customers demand with minimum cost and emission. Fuel cost has straight relationship with energy cost. This paper presents an advanced parallelized particle swarm optimization algorithm (PSPSO) for finding optimal combination of power generation units that minimizes the total fuel cost and emission. In this algorithm, time requirements for solving complex large-scale CEED problem can be substantially reduced using parallel computation and it performs the update of positions and velocities in the end of each iteration. The proposed approach is applied on four test systems and compared with other techniques. The results represent that PSPSO has better convergence than other techniques.

Abstract The implementation of occupancy-controlled and daylighting-dimmed lighting systems has an impact on the energy consumption of residential buildings. The BAC factor method of EN ISO 52120-1 estimates that 8% of the lighting energy can be saved compared to conventional manual control. However, it is assumed that their ability to potentially lower the lighting energy consumption is strongly related to external factors, such as the extent of daylight entrance and the behaviour of the inhabitants. By means of simulations in EnergyPlus, the performances of automated and manual lighting control are compared for an apartment and semi-detached building located in Brussels (Belgium) with variation in the occupant behaviour and orientation. It appears that an automated lighting control including 0-100% dimmer reduces the lighting energy demand for all investigated cases with savings up to 38.4%, whereas a similar control without dimmer does not necessarily reduce the lighting electricity demand. However, the results show a considerable variation, making prediction methods as the BAC factor method highly inaccurate. The actual relative energy performance depends on the automation system, type of building, orientation and occupant behaviour (i.e. number of inhabitants and occupancy rate). Hereby, the number of inhabitants has the most considerable impact on the relative energy performances with differences up to 50%, while the occupancy rate shows a significant correlation, especially for low numbers of inhabitants.

Abstract In many visions and roadmaps, there is a broad agreement that fuel cells – both for stationary and mobile applications – are the key technology to allow the development of a hydrogen infrastructure. Furthermore, this development is generally thought to be based on a gradual, decentralised evolution. Nevertheless, in this paper it is argued that, taking into account the entire hydrogen chain (production, transport, storage, distribution and end-use), this decentralised fuel-cell based philosophy shows some serious flaws. Therefore, a new hydrogen-transition approach was pushed forward: mixing in of hydrogen into the natural-gas bulk. Using Flanders – the Northern part of Belgium – as a case study, the development of a transitory hydrogen infrastructure has been studied, taking into account the entire hydrogen chain and its dynamics, from production to end use. In a next step, this transition is being quantified. An optimisation model has been developed using Matlab and the commercial solvers GAMS and CPLEX. Following a mixed-integer linear-programming approach, this model is able to determine the economically optimal hydrogen-production mix and operational behaviour of each hydrogen-production plant separately. The model then allows gaining valuable insights in the importance of storage and the influence of fuel prices and carbon taxes with regard to the development of an early hydrogen economy.

AbstractThe presented synthesis of the title compounds (II), (IV), (VI) and (VIII) is extremely simple and highly efficient without the use of any chromatographic purification.

Nowadays, a large part of energy is provided by steam turbines; thus, increasing the efficiency and improving the steam turbines performance are of special importance. The presence of the liquid phase in the low-pressure stage of the steam turbine can cause energy loss, efficiency drop, and erosion/corrosion problems; therefore, one of the essential issues is to identify wet steam flow and try to reduce condensation loss. In order to decrease the liquid fraction, the drainage groove technique can be applied. The drainage groove sucks the water droplets from the turbine blade surface and drains them into the condenser. In this study, the effect of the drainage groove location on the surface of steam turbine blades has been investigated on the condensation, droplet radius, inlet mass flow, erosion rate, liquid drainage ratio, condensation losses, and total drainage ratio. For modeling the condensing flow, the Eulerian–Eulerian approach has been applied. The results show that the location of the drainage groove affects the groove performance and flow pattern in the turbine blade. In the selected drainage, the liquid drainage ratio, condensation losses, and erosion rate are reduced by 7.6%, 12%, and 88%, respectively, compared with the no-drainage groove case. Also, the total drainage ratio is 7.2% in the selected drainage. The outcomes of the present work have been a major step forward in the techniques having a great influence on the lifetime, repair and maintenance, and the output power of steam power generation facilities.

Abstract Commercial potential in developing countries has always received a great attention from international investors, but this is not the case in Waste-to-Energy sector. Waste-to-Energy is bound up with various uncertainties rooted in its long-term nature therefore incorporating risks regarding political matters in developing countries makes it more complex. The present study substantiates the incompatibility of classic valuation methods in risky projects. Consequently, to deal with the riskiness of Waste-to-Energy investment in less developed countries, the combination of binomial tree analysis and Decoupled NPV is proposed. The hybrid approach is deployed to value a Waste-to-Energy project in Iran, and all evidence attest to the robustness of the method. The contribution of this paper can open up new vistas for investing in Waste-to-Energy industry, thus abating the catastrophic effects of landfill gas emissions.

Abstract The influence of the solid-phase wall boundary condition was investigated in Eulerian–Eulerian numerical simulations of a bubbling fluidized bed. Parametric studies of the particle–wall restitution coefficient and specularity coefficient were performed to evaluate their impact on the predicted flow hydrodynamics in terms of bed expansion, local voidage, and solid velocity. Both two- and three-dimensional simulations were conducted and compared with available experimental data on solid velocity and bubble properties. It is found that the wall effect plays an important role in CFD models. Such factors as the voidage at the bubble boundary, averaging method, and minimum bubble size also influence the mean bubble diameter.