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Abstract The energetic and exergetic performance of a solar thermal power and ejector-absorption refrigeration system is investigated. R141b, R600a, R290, R717 and R143a were employed as the working fluids for ORC and NH 3 -LiNO 3 was utilized in the ejector-absorption cycle for cooling production. The energetic and exergetic output of PTC driven combined power and refrigeration cycle were evaluated along with the calculation of thermodynamic irreversibility. The distribution of solar exergy input to the cycle in term of exergy produced, destroyed due to irreversibility, and loss due to thermal exhaust to the ambient was computed and compared with the traditional energy distribution. The maximum exergy was destroyed in the PTC where it amounts to 79.61% of the overall exergy destruction. The conversion of solar exergy input to the cycle exergy output was best (14.6%) for R141b fluid and worst (3.9%) for R143a fluid. Parametric analysis of the results reveals that Solar beam radiation (SBR), turbine inlet pressure (TIP), ORC pump inlet temperature, heat transfer fluid (HTF) temperature at the inlet of PTC, and the selection of ORC working fluid have the significant effect on the energetic and exergetic outputs of solar thermal power and ejector-absorption cooling system. Highlights • A relatively new solar based combined power and cooling cycle is proposed. • Proposed cycle is analysed from both energy and exegy point of view. • The effect of sum influencing operating parameters was ascertained on the cycle performance. • Maximum thermodynamic irreversibility was found in solar field followed by HRVG and condenser of ORC cycle. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, exergy method is applied to analyze the gas turbine cycle cogeneration with inlet air cooling and evaporative aftercooling of the compressor discharge. The exergy destruction rate in each component of cogeneration is evaluated in detail. The effects of some main parameters on the exergy destruction and exergy efficiency of the cycle are investigated. The most significant exergy destruction rates in the cycle are in combustion chamber, heat recovery steam generator and regenerative heat exchanger. The overall pressure ratio and turbine inlet temperature have significant effect on exergy destruction in most of the components of cogeneration. The results obtained from the analysis show that inlet air cooling along with evaporative aftercooling has an obvious increase in the energy and exergy efficiency compared to the basic gas turbine cycle cogeneration. It is further shown that the first-law efficiency, power to heat ratio and exergy efficiency of the cogeneration cycle significantly vary with the change in overall pressure ratio and turbine inlet temperature but the change in process heat pressure shows small variation in these parameters. [Copyright &y& Elsevier]

A novel combined power and cooling cycle consisting of the ejector refrigeration cycle (ERC) and organic Rankine cycle (ORC) is proposed to enhance the overall efficiency of wet-ethanol fuelled homogeneous charge compression ignition (HCCI) engine. The proposed combined system of cooling-power cogeneration was simulated by Engineering Equation Solver (EES) software. Combined system responses to altering the operative conditions on the energy and exergy performances are ascertained to obtain guidance for system design. The results are computed for R134a, R290, and R600a working fluids. Increase in turbocharger pressure ratio from 2.5 to 3.5 raises the thermal efficiency of cooling-power cogeneration from 47.87% to 50.09% when R134a is used as working fluid. Cooling capacity and exergy of refrigeration are decreased by greater than 2.0% in case of R134a operated system when the vapor generator pressure is elevated from 1800 kPa to 2200 kPa. Increase in evaporator pressure of ERC from 327.4 kPa to 348.7 kPa is greatly beneficial to thermodynamic performance of cogeneration and its cooling capacity is improved by 11.34% when R134a is utilized as the working. When P Evap rises from 175.7 kPa to 186.9 kPa and R600a is employed as the working fluid, the cooling capacity is increased by 12.58%. • A novel cooling-power cogeneration system driven by HCCI engine exhaust is proposed. • Energy and exergy methods are employed to analyze the performance of proposed system. • Thermal efficiency raises (47.87%–50.09%) with pressure ratio rises from 2.5 to 3.5 • Rise in ERC evaporator pressure increases the cooling capacity by 11.34% for R134a. [ABSTRACT FROM AUTHOR]

Automatic defect detection is gaining huge importance in photovoltaic (PV) field due to limited application of manual/visual inspection and rising production quantities of PV modules. This study is conducted for automatic detection of PV module defects in electroluminescence (EL) images. We presented a novel approach using light convolutional neural network architecture for recognizing defects in EL images which achieves state of the art results of 93.02% on solar cell dataset of EL images. It requires less computational power and time. It can work on an ordinary CPU computer while maintaining real time speed. It takes only 8.07 ms for predicting one image. For proposing light architecture, we perform extensive experimentation on series of architectures. Moreover, we evaluate data augmentation operations to deal with data scarcity. Overfitting appears a significant problem; thus, we adopt appropriate strategies to generalize model. The impact of each strategy is presented. In addition, cracking patterns and defects that can appear in EL images are reviewed; which will help to label new images appropriately for predicting specific defect types upon availability of large data. The proposed framework is experimentally applied in lab and can help for automatic defect detection in field and industry. • A framework using CNN is proposed for automatic detection of defects in PV cells. • It achieved state of the art results of 93.02% accuracy on EL image dataset. • It can work on ordinary CPU computer while maintaining real time speed (8.07 ms). • Data augmentation operations are evaluated that increase accuracy up to 6.5%. • Defect types appeared in EL images are discussed that can help for manual labelling. [ABSTRACT FROM AUTHOR]