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Abstract Wind is a promising sustainable energy resource that can help in reducing the dependence on fossil fuels. Models and tools can be effectively used to assess the resource availability, the possible exploitation, and the environmental impact. The aim of this work is to propose an Environmental Decision Support System (EDSS) for the sustainable design of wind power plants both in terms of the site selection over a regional territory and of the optimal technology to be installed. Specifically, the proposed EDSS is suited to territories with a complex orography (such as several regions of the Mediterranean coasts), and for the installation of plants in the class of power between 500 kW and 1000 kW. The different EDSS modules are applied to a specific case study, supporting the decision maker on the exploitation of wind power plants in the Savona District, Liguria Region, Italy.

Abstract In this work, a novel approach has been presented to predict real-time maximum power, conversion efficiency, daily, monthly and yearly energy production of photovoltaic modules operating outdoor from dawning to evening under changing conditions of illumination and temperature. One-diode electronic circuit with five irradiance and temperature dependent model-parameters has been used to describe the photovoltaic module. Temporal monitoring of temperature and module photovoltaic metrics, during one reference day, has been used to solve the system of non-linear equations corresponding to key-points of current-voltage curve to determine real-time values of model-parameters. New analytical formulas have been proposed to reproduce variations of daytime values of model-parameters as functions of effective irradiance and module temperature for all days of the year. To evaluate accuracy of our predictive approach, meteorological and photovoltaic data recorded by NREL researchers for four PV modules coming from different technologies, which were operating outdoor at Cocoa (Florida) and Eugene (Oregon) during one year have been used. Predicted values of current-voltage characteristics, maximum power and efficiency at arbitrary times in arbitrary days have been compared to respective experimental values. NRMSE and NE have also been calculated to find that these normalized indicators have not exceeded 3%.

This paper reports a practical implementation for maximum power point tracking (MPPT) of a thermoelectric generator (TEG) module using sliding mode control. The principal goal is to apply a robust technique of control to ensure maximum power transfer towards the load through a boost converter. On the one hand, the proposed technique improves rapidly the effect of load variation, and on the other hand, it increases the overall performance of the system. The MPPT control is modeled in Simulink/Matlab with the theoretical Models of a TEG module and a boost converter. Simulation results give the sliding mode control performance under different temperature gradients. Hardware based on an Arduino card is implemented, where the experimental results are presented and analyzed. The experimental results are compared with simulation data where a good agreement is observed. Finally, the results show the effectiveness and the robustness of sliding mode control.

In this paper, an optimization approach of a small horizontal axis wind turbine based on BEM theory including De Vries and Shen et al. tip loss corrections is proposed. The optimal blade geometry was obtained by maximizing the power coefficient along the blade using the optimal angle of attack and the optimal tip speed ratio. The Newton’s iterative method applied to axial induction factor was used to solve the problem. This study was conducted for a NACA4418 small wind turbine, at low wind velocity. Among the two used tip loss corrections, the De Vries correction was found to be the most suitable for this blade optimization method. The optimal design was obtained for a tip speed ratio of 5 and has recorded a power coefficient equal to 0.463.

Abstract This paper shows a novel stochastic maximum power point tracking (MPPT) control for photovoltaic (PV) generators. The PV generator is taken disconnected from the grid, i.e., it considers a direct-current (DC) microgrid supplying varying loads. In this setting, we examine how random, varying loads affect the stability and efficiency of the PV generator. The load changes its value according to a Markov chain, the main assumption of this paper. PV generators are complex nonlinear devices, a challenge from the modeling viewpoint. An alternative becomes converting the nonlinear PV generator model into a Takagi-Sugeno (T-S) fuzzy model. The resulting stochastic T-S fuzzy system has its stability characterized by a condition written in linear matrix inequalities (LMIs). The usefulness of our approach is illustrated by simulating the PV generator model fed with real-time weather data collected in Brazil. The corresponding data indicated that the MPPT efficiency was greater than 99.5%, thereby outperforming other methods from the literature. The corresponding data confirm that the DC-DC converter circuit was able to track maximum power from the PV generator against random load variations. Comparisons with other methods indicate the potential of our approach for PV generators.

The main objective of this study is to estimate growth kinetic constants and the concentration of "active" attached biomass in two anaerobic thermophilic reactors which contain different initial sizes of immobilized anaerobic mixed cultures and decompose distillery wastewater. This paper studies the substrate decomposition in two lab-scale fixed-bed reactors operating at batch conditions with corrugated tubes as support media. It can be demonstrated that high micro-organisms-substrate ratios favor the degradation activity of the different anaerobic cultures, allowing the stable operation without lag-phases and giving better quality in effluent. The kinetic parameters obtained--maximum specific growth rates (mu(max)), non-biodegradable substrate (S(NB)) and "active or viable biomass" concentrations (X(V0))--were obtained by applying the Romero kinetic model [L.I. Romero, 1991. Desarrollo de un modelo matemático general para los procesos fermentativos, Cinética de la degradación anaerobia, Ph.D. Thesis, University of Cádiz (Spain), Serv. Pub. Univ. Cádiz], with COD as substrate and methane (CH4) as the main product of the anaerobic process. This method is suitable to calculate and to differentiate the main kinetic parameters of both the total anaerobic mixed culture and the methanogenic population. Comparison of experimental measured concentration of volatile attached solids (VS(att)) in both reactors with the estimated "active" biomass concentrations obtained by applying Romero kinetic model [L.I. Romero, 1991. Desarrollo de un modelo matemático general para los procesos fermentativos, Cinética de la degradación anaerobia, Ph.D. Thesis, University of Cádiz (Spain), Serv. Pub. Univ. Cádiz] shows that a large amount of inert matter is present in the fixed-bed reactor.

Second-generation bioethanol is considered a suitable option for replacing fossil fuels. Agricultural residues are being studied as feedstocks for sugar generation, which are in turn converted into ethanol. Among them, barley straw (BS) is a promising raw material, due to its high abundance, lignocellulosic composition and lack of other practical applications. Under these assumptions, the central aim of this study is to suggest an efficient bioethanol production scheme from BS at different levels of integration in co-fermentation with Escherichia coli SL100, including separate hydrolysis and co-fermentation (SHCF), simultaneous saccharification and co-fermentation (SSCF), and presaccharification and simultaneous saccharification and co-fermentation (PSSCF), using the water-insoluble solid (WIS) and slurry fractions obtained after steam explosion (SE) pretreatment. The best results in terms of ethanol yield were achieved following the SHCF process, using the WIS and the slurry as substrates, with yields of 89.1% and 78.8% of the theoretical maximum, respectively. Considering all of the above points, the following scheme is proposed for the conversion of BS into ethanol: SE pretreatment (160 °C, 30 min) of BS previously soaked overnight in 2.88% w/v phosphoric acid solution, filtration of the slurry, followed by enzymatic hydrolysis and co-fermentation of the two fractions obtained separately, with previous detoxification of the prehydrolysate with ammonium hydroxide (5 N). Under these conditions, 19.43 g of bioethanol was produced from 100 g of BS.

Liquid fuel nuclear reactors offer innovative possibilities in terms of nuclear reactor designs and passive safety systems. Molten Salts Reactors (MSRs) with a fast spectrum are a particular type of these reactors using liquid fuel. MSFRs often involve large open cavities in their core in which the liquid fuel circulates at a high speed to transport the heat generated by the nuclear reactions into the heat exchangers. This high-speed flow yields a turbulent field with large Reynolds numbers in the reactor core. Since the nuclear power, the neutron precursor’s transport and the thermal exchanges are strongly coupled in the MSFR’s core cavity, having accurate turbulent models for the liquid fuel flow is necessary to avoid introducing significant errors in the numerical simulations of these reactors. Nonetheless, high-accuracy simulations of the turbulent flow field in the reactor cavity of these reactors are usually prohibitively expensive in terms of computational resources, especially when performing multiphysics numerical calculations. Therefore, in this work, we propose a novel method using a modified genetic algorithm to optimize the calculation of the Reynolds Shear Stress Tensor (RST) used for turbulence modeling. The proposed optimization methodology is particularly suitable for advanced liquid fuel reactors such as the MSFRs since it allows the development of high-accuracy but still low-computational-cost turbulence models for the liquid fuel. We demonstrate the applicability of this approach by developing high accuracy Reynolds-Averaged Navier–Stokes (RANS) models (averaged flow error less than 5%) for a low and a large aspect ratio in a Backward-Facing Step (BFS) section particularly challenging for RANS models. The newly developed turbulence models better capture the flow field after the boundary layer tipping, over the extent of the recirculation bubble, and near the boundary layer reattachment region in both BFS configurations. The main reason for these improvements is that the developed models better capture the flow field turbulent anisotropy in the bulk region of the BFS. Then, we illustrate the interest in using this turbulence modeling approach for the case of an MSFR by quantifying the impact of the turbulence modeling on the reactor key parameters.