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Cogeneration plants, which simultaneously produce electricity and heat energy, have been introduced increasingly for commercial and domestic applications in Korea because of their energy efficiency. The optimal plant configuration of a specific commercial building can be determined by selecting the sizes and the number of cogeneration systems and the auxiliary equipment based on the annual demands of electricity, heating and cooling. In this study, a mixed-integer, linear programming, utilizing the branch and bound algorithm was used to obtain the optimal solution. Both the optimal configuration system equipment and the optimal operational mode were determined based on the annual cost method for the installation of a cogeneration system to a hospital and a group of apartments in Seoul, Korea. In addition, the economic evaluation for the optimal cogeneration system depending on the fuel tariff system was calculated. A short payback period and higher internal rate of return on the initial investment were found to be essential for the adoption of cogeneration plants to hospitals and apartments.

Abstract Liquefaction of poly-isoprene based rubber (PIR) was performed using ethanol as a solvent for the production of liquid fuel and chemicals. An autoclave batch reactor was used to perform the ethanolysis of PIR at different temperature ranges (250–375 °C), with different ethanol to PIR ratio (0.5:1 to 4:1), and at different reaction times (15–75mins). The experimental results showed that a maximum yield of 86 wt % was achieved at temperature of 325 °C, ethanol to PIR ratio 1/1, and reaction time of 30 min. This liquid oil yield is about 14% higher than the yield obtained from the pyrolysis of PIR at 500 °C and about 10% higher than the yield obtained from hydrothermal liquefaction of PIR at 375 °C. Moreover, the utilization of ethanol in the process was also incorporated and product yields were redefined. Furthermore, ethanol contributed to enhance the quality of liquid-oil, particularly in term of viscosity, acidity, and energy density. Furthermore, the FTIR analysis showed methyl and methylene were most dominating functional groups found in the liquid product and GCMS analysis identified that they were presented by alkenes, aromatics, and alkyls.

Abstract Effect of inclination angle to the thermal performance of a heat pipe photovoltaic/thermal system (HP-PV/T) system was rarely reported. In the present study, a HP-PV/T system was firstly constructed in an Enthalpy Difference Laboratory, where inclination angle was experimentally managed as the only variable. Meanwhile, a comprehensive numerical model for the HP-PV/T system was developed to validate the experiments. Particularly, a 3D model for the inclined heat pipe was also firstly involved. The simulation results show that liquid film thickness within the condenser or the evaporator stabilizes at a constant value at inclining condition. The relative filmwise thermal resistance of the condenser decreases first and then increases with inclination angle; while the evaporator shows an opposite trend to the condenser. The overall thermal resistance of solar heat pipe is mainly determined by the evaporator while the evaporator is mainly determined by the effective height of the liquid pool. The experimental and simulation results both indicate that the optimum inclination angle is 40°. The proposed model agrees well with the experimental results at big inclination angles (≥20°), it is practicable to reveal the influence of inclination angle to the thermal performance of a HP-PV/T system.

Abstract The high penetration of Renewable Energy Sources (RES) makes the power system unreliable due to its uncertain nature and to deal with this uncertainties installation of an Energy Storage System (ESS) is suggested. In this paper, the quantifying impact of ESS capacity on power system network reliability and relieving the congestion is analyzed. The proposed reliability assessment and congestion relief problem is formulated by considering generation cost and demand interruption cost for N-1 contingency criteria. The proposed algorithm manages the optimal charging and discharging of ESS to mitigate the uncertainties associated with RES and relieving the congestion. The impact of ESS capacity on reliability indices Expected Energy not Supplied (EENS) and Expected interruption Cost (ECOST) for Generating Companies (GENCOs), Transmission Companies (TRANSCOs), customers, and entire power system are calculated. The appropriate size of ESS is selected by the trade-off between investment cost of ESS and percentage change in EENS and ECOST value for the entire power system, GENCOs, TRANSCOs, and customers. The effectiveness of the proposed approach is tested on the modified IEEE RTS 24 bus system. The problem is modeled in the Generic Algebraic Solver (GAMS) environment and solved using CONOPT as an NLP solver.

Abstract To reduce deployment and maintenance costs, a novel wave energy converter (WEC) is proposed for mooring-less sensor buoys. The design concept is based on a small size WEC capable of harvesting wave energy without mooring, which can reduce the installation cost. The proposed wave energy converter consists of a submerged and a floating body, and the submerged body is a self-rectifying wave-induced turbine that uses the rise and fall of waves to turn a rotor. The rotor of the turbine has flap-type blades, which allows a self-rectifying rotation with rising and falling of waves. In this paper, the dynamics of the system is modeled by hydrodynamic equations, and simulations are carried out based on the dynamic model to determine the optimal design parameters of the system. In addition, the power generation in regular and irregular wave conditions and efficiency in irregular waves of the system are estimated. To verify the results of the simulation, a prototype of the system is implemented and tested in a sea trial. The results demonstrate the feasibility of the proposed wave energy converter.

Abstract Forecasting the future price of crude oil, which has an important role in the global economy, is considered as a hot matter for both investment companies and governments. However, forecasting the price of crude oil with high precision is indeed a challenging task because of the nonlinear dynamics of the crude oil time series, including chaotic behavior and inherent fractality. In this study, a new forecasting model based on support vector regression (SVR) with a wrapper-based feature selection approach using multi-objective optimization technique is developed to deal with this challenge. In our model, features based on technical indicators such as simple moving average (SMA), exponential moving average (EMA), and Kaufman’s adaptive moving average (KAMA) are utilized. SMA, EMA, and KAMA indicators are obtained from Brent crude oil closing prices under different parameters. The features based on SMA and EMA indicators are formed by changing the period values between 3 and 10. The features based on the KAMA indicator are obtained by changing the efficiency ratio (ER) period value, which is considered as fractality efficiency, between 3 and 10. The features are selected by the wrapper-based approach consisting of multi-objective particle swarm optimization (MOPSO) and radial basis function based SVR (RBFSVR) techniques considering both the mean absolute percentage error (MAPE) and Theil’s U values. The obtained empirical results show that the proposed forecasting model can capture the nonlinear properties of crude oil time series, and that better forecasting performance can be obtained in terms of precision and volatility than the other current forecasting models.

Abstract This paper aims to offer an efficient, simple and accurate cubic-order subparametric finite element technique utilizing 2-D automated mesh generator for microwave applications. The proposed technique utilizes the best discretization procedure, the finest quadrature rule and an excellent subparametric finite element algorithm for obtaining the numerical solutions of the Helmholtz equation. The high-quality automated mesh generator MATLAB code developed for the present work using HOmesh2d.m and CurvedHOmesh2d.m are provided. This approach utilizes up to cubic-order triangular mesh for arbitrary waveguide structures. The meshes with one side curved cubic-order triangular elements are proposed with parabolic arcs for curved waveguide structures. For regular waveguide structures with sharp edges having singularities, the utilization of unstructured cubic-order triangular meshes with refinement for the technique is proposed. The method is demonstrated for six different waveguide structures and the outcomes obtained are compared with the best available numerical or analytical results. The results show that the proposed technique produces an efficient and most accurate finite element results for the finite element analysis performed for waveguide structures with singularities and curved geometries as there is no curvature loss. Thus, the proposed subparametric finite element technique with the automated mesh generator is verified to yield a viable approach for getting the most precise numerical result for the 2-D Helmholtz equation in distinct waveguide cross-sections. Therefore, this approach can be used to produce the most efficient energy transfer for microwave applications.