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• W2964792319 abstract "With development of society, energy needs and the warnings of global warming have become two main focuses in the considerable aspects of human life. Currently, researchers need to concentrate on applying innovative methods for controlling the growth of energy use. Besides improving the energy efficiency by reducing energy consumption, energy quality issues have become a key topic of interest. Energy quality management (EQM) is a technique that aims at optimally utilizing the exergy content of various energy sources.This chapter introduces EQM in a relative systematic scope. Firstly, this chapter provides a definition of “energy quality,” and then it makes an explanation about the concept of EQM. Subsequently, EQM is introduced in two application fields: built environment and energy conversion technology. In this part, five case studies are presented. A brief description of each is shown below:Renewable energy source is unpredictable and highly depends on climatic conditions, thus energy system reliability needs to be considered for the renewable energy systems. This case study defines the system reliability as a constraint function. EQM is capable of guiding users to make optimizations for energy systems under the predefined constraint.For this case study, EQM aims to explore the optimum energy system that contains solar technologies in order to identify the suitable solar energy utilization pattern for different district typologies. The optimum system is defined with minimum life cycle CO2 emissions and cost as well as maximum exergy efficiency (EE). Then, the effects of different solar energy parameters on the solar utilization patterns for these districts are investigated.As an important means to realize the energetic complementarity and improve the efficiency of renewable resources, the stand-alone microgrid (SAMG) system gains attention increasingly, especially in islands and remote areas. As a testing study, EQM is proposed to formulate for medium voltage (MV) SAMG in the case.For this case, EQM concepts are applied to an ejector refrigeration system (ERS) that uses natural refrigerant R600a as the working fluid and has 100 kW cooling capacity (CC). The system is investigated from thermodynamic and thermoeconomic perspectives. To take EQM for such a system, the objective function is defined as the sum of the costs of brine side fluids, electricity (EL), and costs related to capital investment and operation and maintenance expense. The pinch point temperatures in the three heat changers are considered as the decision variables, with two different economic scenarios imposed to the system. An iteration technique is employed to minimize the objective function. It shows that the optimized objective function is reduced by 8.1 and 7.5%, respectively, compared to the nonoptimized cases for the two scenarios.In this case study, a new ejector refrigeration system (NERS) using zeotropic mixture is proposed. R32/R134a, R32/R152a, R134a/R142b, R152a/R142b, R290/R600a, and R600a/R600 are selected as the working fluids in the analysis. The comparison between this NERS and the conventional ejector refrigeration system (CERS) is made under the same operating condition. It is found that this new system has higher coefficient of performances (COPs) than the CERS. The effects of operating conditions and the composition on the performance of this new system are presented. EQM is carried out to study the loss within each component of the system. It is observed that the irreversibility in the EJ represents more than 50% of the total exergy loss.As the last part, a novel type of advanced EQM based on the conventional EQM is included. Also, an ERS is set as an example to explain how the advanced EQM works. The advanced EQM for an ERS might avoid 35% of the overall exergy destruction in the whole system. It can be regarded as a valuable supplement to the conventional EQM in future." @default.
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• W2964792319 date "2019-01-01" @default.
• W2964792319 modified "2023-10-18" @default.
• W2964792319 title "53CONTROLLING TYPE 1 ERROR RATE IN CNV GENE-SET ENRICHMENT TESTS" @default.
• W2964792319 doi "https://doi.org/10.1016/j.euroneuro.2018.08.060" @default.
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