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This study focused on investigating the bottoming power cycles operating with CO2-based binary mixture, taking into account exergetic, economic and exergo-environmental impact indices. The main intent is to assess the benefits of employing a CO2-based mixture working fluid in closed Brayton bottoming power cycles in comparison with pure CO2 working fluid. Firstly, selection criteria for the choice of suitable additive compound for CO2-based binary mixture is delineated and the composition of the binary mixture is decided based on required cycle minimum temperature. The decided CO2-C7H8 binary mixture with a 0.9 mole fraction of CO2 is analyzed in two cycle configurations: Simple regenerative cycle (SRC) and Partial heating cycle (PHC). Comparative analysis among two configurations with selected working fluid are carried out. Thermodynamic analyses at varying cycle pressure ratio shows that cycle with CO2-C7H8 mixture shows maximum power output and exergy efficiency at rather higher cycle pressure ratio compared to pure CO2 power cycles. PHC with CO2-C7H8 mixture shows 28.68% increment in exergy efficiency with the levelized cost of electricity (LCOE) 21.62% higher than pure CO2 PHC. Whereas, SRC with CO2-C7H8 mixture shows 25.17% increment in exergy efficiency with LCOE 57.14% higher than pure CO2 SRC. Besides showing lower economic value, cycles with a CO2-C7H8 mixture saves larger CO2 emissions and also shows greater exergo-environmental impact improvement and plant sustainability index.

This study focused on investigating the bottoming power cycles operating with CO2-based binary mixture, taking into account exergetic, economic and exergo-environmental impact indices. The main intent is to assess the benefits of employing a CO2-based mixture working fluid in closed Brayton bottoming power cycles in comparison with pure CO2 working fluid. Firstly, selection criteria for the choice of suitable additive compound for CO2-based binary mixture is delineated and the composition of the binary mixture is decided based on required cycle minimum temperature. The decided CO2-C7H8 binary mixture with a 0.9 mole fraction of CO2 is analyzed in two cycle configurations: Simple regenerative cycle (SRC) and Partial heating cycle (PHC). Comparative analysis among two configurations with selected working fluid are carried out. Thermodynamic analyses at varying cycle pressure ratio shows that cycle with CO2-C7H8 mixture shows maximum power output and exergy efficiency at rather higher cycle pressure ratio compared to pure CO2 power cycles. PHC with CO2-C7H8 mixture shows 28.68% increment in exergy efficiency with the levelized cost of electricity (LCOE) 21.62% higher than pure CO2 PHC. Whereas, SRC with CO2-C7H8 mixture shows 25.17% increment in exergy efficiency with LCOE 57.14% higher than pure CO2 SRC. Besides showing lower economic value, cycles with a CO2-C7H8 mixture saves larger CO2 emissions and also shows greater exergo-environmental impact improvement and plant sustainability index.

AbstractThis article studies the unsteady MHD free flow of a Casson fluid past an oscillating vertical plate with constant wall temperature. The fluid is electrically conducting and passing through a porous medium. This phenomenon is modelled in the form of partial differential equations with initial and boundary conditions. Some suitable non-dimensional variables are introduced. The corresponding non-dimensional equations with conditions are solved using the Laplace transform technique. Exact solutions for velocity and energy are obtained. They are expressed in simple forms in terms of exponential and complementary error functions of Gauss. It is found that they satisfy governing equations and corresponding conditions, and are reduced to similar solutions for Newtonian fluids as a special case. Expressions for skin-friction and Nusselt number are also evaluated. Computations are carried out and the results are analysed for emerging flow parameters.

AbstractThis article studies the unsteady MHD free flow of a Casson fluid past an oscillating vertical plate with constant wall temperature. The fluid is electrically conducting and passing through a porous medium. This phenomenon is modelled in the form of partial differential equations with initial and boundary conditions. Some suitable non-dimensional variables are introduced. The corresponding non-dimensional equations with conditions are solved using the Laplace transform technique. Exact solutions for velocity and energy are obtained. They are expressed in simple forms in terms of exponential and complementary error functions of Gauss. It is found that they satisfy governing equations and corresponding conditions, and are reduced to similar solutions for Newtonian fluids as a special case. Expressions for skin-friction and Nusselt number are also evaluated. Computations are carried out and the results are analysed for emerging flow parameters.

AbstractPower companies are responsible for producing and transferring the required amount of electricity from grid stations to individual households. Many countries suffer huge losses in billions of dollars due to non-technical loss (NTL) in power supply companies. To deal with NTL, many machine learning classifiers have been employed in recent time. However, few has been studied about the performance evaluation metrics that are used in NTL detection to evaluate how good or bad the classifier is in predicting the non-technical loss. This paper first uses three classifiers: random forest,K-nearest neighbors and linear support vector machine to predict the occurrence of NTL in a real dataset of an electric supply company containing approximately 80,000 monthly consumption records. Then, it computes 14 performance evaluation metrics across the three classifiers and identify the key scientific relationships between them. These relationships provide insights into deciding which classifier can be more useful under given scenarios for NTL detection. This work can be proved to be a baseline not only for the NTL detection in power industry but also for the selection of appropriate performance evaluation metrics for NTL detection.

AbstractPower companies are responsible for producing and transferring the required amount of electricity from grid stations to individual households. Many countries suffer huge losses in billions of dollars due to non-technical loss (NTL) in power supply companies. To deal with NTL, many machine learning classifiers have been employed in recent time. However, few has been studied about the performance evaluation metrics that are used in NTL detection to evaluate how good or bad the classifier is in predicting the non-technical loss. This paper first uses three classifiers: random forest,K-nearest neighbors and linear support vector machine to predict the occurrence of NTL in a real dataset of an electric supply company containing approximately 80,000 monthly consumption records. Then, it computes 14 performance evaluation metrics across the three classifiers and identify the key scientific relationships between them. These relationships provide insights into deciding which classifier can be more useful under given scenarios for NTL detection. This work can be proved to be a baseline not only for the NTL detection in power industry but also for the selection of appropriate performance evaluation metrics for NTL detection.

Thin (approximately 10 nm) oxide buffer layers grown over lead-halide perovskite device stacks are critical for protecting the perovskite against mechanical and environmental damage. However, the limited perovskite stability restricts the processing methods and temperatures (<=110 C) that can be used to deposit the oxide overlayers, with the latter limiting the electronic properties of the oxides achievable. In this work, we demonstrate an alternative to existing methods that can grow pinhole-free TiOx (x = 2.00+/-0.05) films with the requisite thickness in <1 min without vacuum. This technique is atmospheric pressure chemical vapor deposition (AP-CVD). The rapid but soft deposition enables growth temperatures of >=180 ��C to be used to coat the perovskite. This is >=70 ��C higher than achievable by current methods and results in more conductive TiOx films, boosting solar cell efficiencies by >2%. Likewise, when AP-CVD SnOx (x ~ 2) is grown on perovskites, there is also minimal damage to the perovskite beneath. The SnOx layer is pinhole-free and conformal, which reduces shunting in devices, and increases steady-state efficiencies from 16.5% (no SnOx) to 19.4% (60 nm SnOx), with fill factors reaching 84%. This work shows AP-CVD to be a versatile technique for growing oxides on thermally-sensitive materials. R.D.R and R.A.J contributed equally. 23 pages. 6 figures