Analysis of advanced supercritical carbon dioxide power cycles with a bottoming cycle for concentrating solar power applications

A number of studies have been performed to assess the potential of using supercritical carbon dioxide (S-CO 2 ) in closed-loop Brayton cycles for power generation. Different configurations have been examined among which recompression and partial cooling configurations have been found very promising, especially for concentrating solar power (CSP) applications. It has been demonstrated that the S-CO 2 Brayton cycle using these configurations is capable of achieving more than 50% efficiency at operating conditions that could be achieved in central receiver tower type CSP systems. Although this efficiency is high, it might be further improved by considering an appropriate bottoming cycle utilizing waste heat from the top S-CO 2 Brayton cycle. The organic Rankine cycle (ORC) is one alternative proposed for this purpose; however, its performance is substantially affected by the selection of the working fluid. In this paper, a simple S-CO 2 Brayton cycle, a recompression S-CO 2 Brayton cycle, and a partial cooling S-CO 2 Brayton cycle are first simulated and compared with the available data in the literature. Then, an ORC is added to each configuration for utilizing the waste heat. Different working fluids are examined for the bottoming cycles and the operating conditions are optimized. The combined cycle efficiencies and turbine expansion ratios are compared to find the appropriate working fluids for each configuration. It is also shown that combined recompression-ORC cycle achieves higher efficiency compared with other configurations

Analysis of Advanced Supercritical Carbon Dioxide Power Cycles for Concentrated Solar Power Applications

Solar power tower technology can achieve higher temperatures than the most common commercial technology using parabolic troughs. In order to take advantage of higher temperatures, new power cycles are needed for generating power at higher efficiencies. Supercritical carbon dioxide (S-CO2) power cycle is one of the alternatives that have been proposed for the future concentrated solar power (CSP) plants due to its high efficiency. On the other hand, carbon dioxide can also be a replacement for current heat transfer fluids (HTFs), i.e. oil, molten salt, and steam. The main disadvantages of the current HTFs are maximum operating temperature limit, required freeze protection units, and complex control systems. However, the main challenge about utilizing s-CO2 as the HTF is to design a receiver that can operate at high operating pressure (about 20 MPa) while maintaining excellent thermal performance. The existing tubular and windowed receivers are not suitable for this application; therefore, an innovative design is required to provide appropriate performance as well as mechanical strength. This research investigates the application of s-CO2 in solar power tower plants. First, a computationally efficient method is developed for designing the heliostat field in a solar power tower plant. Then, an innovative numerical approach is introduced to distribute the heat flux uniformly on the receiver surface. Next, different power cycles utilizing s-CO2 as the working fluid are analyzed. It is shown that including an appropriate bottoming cycle can further increase the power cycle efficiency. In the next step, a thermal receiver is designed based on compact heat exchanger (CHE) technology utilizing s-CO2 as the HTF. Finally, a 3MWth cavity receiver is designed using the CHE receivers as individual panels receiving solar flux from the heliostat field. Convective and radiative heat transfer models are employed to calculate bulk fluid and surface temperatures. The receiver efficiency is obtained as 80%, which can be further improved by optimizing the geometry of the cavity

Supercritical fluids and their applications in power generation

Supercritical fluids have been studied and used as the working fluids in power generation system for both high- and low-grade heat conversions. Low-grade heat sources, typically defined as below 300 ºC, are abundantly available as industrial waste heat, solar thermal, and geothermal, to name a few. However, they are under-exploited for power conversion because of the low conversion efficiency. Technologies that allow the efficient conversion of low-grade heat into mechanical or electrical power are very important to develop. First part of this chapter investigates the potential of supercritical Rankine cycles in the conversion of low-grade heat to power, while the second part discusses supercritical fluids used in higher grade heat conversion system. The selection of supercritical working fluids for a supercritical Rankine cycle is of key importance. This chapter discusses supercritical fluids fundamentals, selection of supercritical working fluids for different heat sources, and the current research, development, and commercial status of supercritical power generation systems

The potential of harnessing solar radiation in Iran: generating solar maps and viability study of PV power plants

This paper assesses the potential of harnessing solar radiation in different regions of Iran. In this regard, solar radiation maps are generated for five different cases: total radiation on a south facing fixed surface tilted at the latitude angle; total radiation on a surface tilted at the latitude angle with East–West tracking; total radiation on a surface tilted at the latitude angle with azimuth tracking; direct beam radiation on a horizontal surface with East–West tracking; and direct beam radiation on a surface with two axis tracking. The first three cases are generally applicable to photovoltaic (PV) power plants while the last two are generally used for concentrating solar power (CSP) plant design. In the second part of the paper, a 5 MW PV power plant is considered for 50 cities of Iran. Capacity factors, electricity generated and annual greenhouse gases emission reductions are compared. The results show a great potential for central and southern parts of the country

Modeling Friction Factor in Pipeline Flow Using a GMDH-type Neural Network

The standard methods of calculating the fluid friction factor, the Colebrook–White and Haaland equations, require iterative solution of an implicit, transcendental function which entails high computational costs for large-scale piping networks while introducing as much as 15% error. This study applies the group method of data handling to the development of an artificial neural network optimized by multi-objective genetic algorithms to find an explicit polynomial model for friction factor. We developed a relatively simple and explicit model for friction factor that performs well over the entire range of applicability of the Colebrook–White equation: Reynolds number from 4,000 to 108 with relative roughness ranging from 5 × 10−6 to 0.05. For a network with only two hidden layers and a total of five neurons, this model was found to have a mean relative error of only 3.4% in comparison with the Colebrook–White equation; a determination coefficient (R2) over the range of input data was calculated to be 0.9954. The accuracy and simplicity of this model may make it preferable to traditional, transcendental representations of fluid friction factor. Further, this method of model development can be applied to any pertinent data-set—that is to say, the model can be tuned to the physical situation and input data range of interest

Modeling friction factor in pipeline flow using a GMDH-type neural network

The standard methods of calculating the fluid friction factor, the Colebrook–White and Haaland equations, require iterative solution of an implicit, transcendental function which entails high computational costs for large-scale piping networks while introducing as much as 15% error. This study applies the group method of data handling to the development of an artificial neural network optimized by multi-objective genetic algorithms to find an explicit polynomial model for friction factor. We developed a relatively simple and explicit model for friction factor that performs well over the entire range of applicability of the Colebrook–White equation: Reynolds number from 4,000 to 108 with relative roughness ranging from 5 × 10−6 to 0.05. For a network with only two hidden layers and a total of five neurons, this model was found to have a mean relative error of only 3.4% in comparison with the Colebrook–White equation; a determination coefficient (R2) over the range of input data was calculated to be 0.9954. The accuracy and simplicity of this model may make it preferable to traditional, transcendental representations of fluid friction factor. Further, this method of model development can be applied to any pertinent data-set—that is to say, the model can be tuned to the physical situation and input data range of interest