Current Issue : October - December Volume : 2012 Issue Number : 4 Articles : 5 Articles
Liquefied natural gas (LNG) will contribute more in the future than in the past to the overall energy supply in the\r\nworld. The paper discusses the application of advanced exergy-based analyses to a recently developed LNG-based\r\ncogeneration system. These analyses include advanced exergetic, advanced exergoeconomic, and advanced\r\nexergoenvironmental analyses in which thermodynamic inefficiencies (exergy destruction), costs, and environmental\r\nimpacts have been split into avoidable and unavoidable parts. With the aid of these analyses, the potentials for\r\nimproving the thermodynamic efficiency and for reducing the overall cost and the overall environmental impact are\r\nrevealed. The objectives of this paper are to demonstrate (a) the potential for generating electricity while regasifying\r\nLNG and (b) some of the capabilities associated with advanced exergy-based methods. The most important\r\nsubsystems and components are identified, and suggestions for improving them are made....
Growth rate of a hydrate layer at the guest/liquid-water interface is analyzed\r\nconsidering the conjugate process of the mass-transfer and hydrate crystal growth.\r\nHydrate-layer growth rate data in the literature are often compiled according to the system\r\nsubcooling (T Teq Tex, where Teq is the equilibrium dissociation temperature of the\r\nhydrate and Tex is the system temperature), suggesting predominant heat transfer\r\nlimitations. In this paper, we investigate how the existing data on hydrate-layer growth is\r\nbetter correlated to mass transfer of the guest species in liquid water in three-phase\r\nequilibrium with bulk guest fluid and hydrate. We have analyzed the conjugate processes\r\nof mass-transfer/hydrate-layer-growth following our previous study on the hydrate crystal\r\ngrowth into liquid water saturated with a guest substance. A dimensionless parameter\r\nrepresenting the hydrate-layer growth rate is derived from the analysis. This analysis is\r\nbased on the idea that the growth rate is controlled by the mass transfer of the\r\nhydrate-guest substance, dissolved in the bulk of liquid water, to the front of the growing\r\nhydrate-layer along the guest/water interface. The variations in the hydrate-layer growth\r\nrate observed in the previous studies are related to the dimensionless parameter....
Artificial Immune System based novel optimization approach is proposes for the solution of Economic Load Dispatch problem with valve point effect. The solution of constraint based Economic Load Dispatch problem is obtained taking valve point effect for thermal units. Artificial Immune System based clonal selection principle is successfully applied for the solution of Economic Load Dispatch Problem. The developed AIS technique uses the total operating cost as objective function and it is represented by affinity measure wherein cloning of antibodies is performed and followed by hyper mutation. Through genetic evolution, the affinity measures are produced and the best individuals are selected for the solution. The proposed algorithm is tested with multiple units (3 and 6) and multi fuel (wind, biodiesel) conditions, with multiple constraints like Nonconvex/non smooth cost functions, ramp rate limits, prohibited operating zones for thermal units results are compared with other prevalent methods like Lambada Iteration and Genentic Algorithm. The results divulge that the method is easy to implement, fast convergent and applicable for the solution of complex ELD problem....
This paper proposes a structure for long-term energy demand forecasting. The\r\nproposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF)\r\nmodel as the forecaster and utilizes the Hodrickââ?¬â??Prescott (HP) filter for extraction of the\r\ntrend and cyclic components of the energy demand series. Besides, the sophisticated\r\ntechnique of mutual information (MI) is employed to select the most relevant input features\r\nwith least possible redundancies for the forecast model. Each generated component by the\r\nHP filter is then modeled through an LLNF model. Starting from an optimal least square\r\nestimation, the local linear model tree (LOLIMOT) learning algorithm increases the\r\ncomplexity of the LLNF model as long as its performance is improved. The proposed\r\nHPLLNF model with MI-based input selection is applied to the problem of long-term\r\nenergy forecasting in three different case studies, including forecasting of the gasoline,\r\ncrude oil and natural gas demand over the next 12 months. The obtained forecasting results\r\nreveal the noteworthy performance of the proposed approach for long-term energy demand\r\nforecasting applications....
Most photovoltaic (PV) generation systems are connected with a utility grid and recognized as supplemental generation resources;\r\nbut in some applications such as microgrid concept, a PV system works as a main resource. To improve the availability of\r\nPV systems, technological development for higher less output fluctuation in normal condition, higher fault tolerance in fault\r\noccurrence, and power demand and supply balancing in isolated condition are required. For these reasons, hybridization of a PV\r\nsystem and an energy storage system (ESS) would become an important technology in the future. This paper presented two kinds\r\nof circuit models, conventional ââ?¬Å?ac-connected PV-ESS,ââ?¬Â and proposed ââ?¬Å?dc-connected PV-ESSââ?¬Â in which ESS is inserted at the dcside\r\nof PV system. This paper also investigated dc-link voltage controlled by dc-dc converter of ESS in dc-connected PV-ESS and\r\nsuitable control systems are also discussed normal, during fault occurrence and isolated operation....
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