Saeb M. Besarati

Multi-Objective Optimization of a Combined Power and Cooling Cycle for Low-Grade and Midgrade Heat Sources

Optimization of thermodynamic cycles is important for the efficient utilization of energy sources; indeed it is more crucial for the cycles utilizing low grade heat sources where the cycle efficiencies are smaller compared to high temperature power cycles. This paper presents the optimization of a combined power/cooling cycle, also known as the Goswami Cycle, which combines the Rankine and absorption refrigeration cycles. The cycle uses a special binary fluid mixture as the working fluid and produces power and refrigeration. In this regard, multiobjective genetic algorithms (GA) are used for Pareto approach optimization of the thermodynamic cycle. The optimization study includes two cases. In the first case the performance of the cycle is evaluated as it is used as a bottoming cycle, and in the second case as it is used as a top cycle utilizing solar energy or geothermal sources. The important thermodynamic objectives that have been considered in this work are, namely, work output, cooling capacity, effective first law and exergy efficiencies. Optimization is carried out by varying the selected

Performance Analysis of a Rankine Cycle Integrated With the Goswami Combined Power and Cooling Cycle

Improving the efficiency of thermodynamic cycles plays a fundamental role in reducing the cost of solar power plants. These plants work normally with Rankine cycles which present some disadvantages due to the thermodynamic behavior of steam at low pressures. These disadvantages can be reduced by introducing alternatives such as combined cycles which combine the best features of each cycle. In this paper, a combined Rankine–Goswami cycle (RGC) is proposed and a thermodynamic analysis is conducted. The Goswami cycle, used as a bottoming cycle, uses ammonia–water mixture as the working fluid and produces power and refrigeration while power is the primary goal. This bottoming cycle, reduces the energy losses in the traditional condenser and eliminates the high specific volume and poor vapor quality presented in the last stages of the lower pressure turbine in the Rankine cycle. In addition, the use of absorption condensation in the Goswami cycle, for regeneration of the strong solution, allows operating the low pressure side of the cycle above atmospheric pressure which eliminates the need for maintaining a vacuum pressure in the condenser. The performance of the proposed combined Rankine–Goswami cycle, under full load, was investigated for applications in parabolic trough solar thermal plants for a range from 40 to 50 MW sizes. A sensitivity analysis to study the effect of the ammonia concentration, condenser pressure, and rectifier concentration on the cycle efficiency, network, and cooling was performed. The results indicate that the proposed RGC provide a difference in net power output between 15.7% and 42.3% for condenser pressures between 1 and 9 bars. The maximum effective first law and exergy efficiencies for an ammonia mass fraction of 0.5 are calculated as 36.7% and 24.7%, respectively, for the base case (no superheater or rectifier process).

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

Abstract 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.

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-CO2) 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-CO2 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-CO2 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-CO2 Brayton cycle, a recompression S-CO2 Brayton cycle, and a partial cooling S-CO2 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.Copyright

Performance of Working Fluids for Power Generation in a Supercritical Organic Rankine Cycle

This paper reports on the performance of various organic refrigerants and their mixtures as working fluids for power generation in a supercritical Rankine cycle (SRC) from geothermal sources. Organic fluids that have zero or very low ozone depletion potential and are environmentally safe are selected for this study. Geothermal source temperature is varied from 125–200°C, and the cooling water temperature is changed from 10–20°C. The effect of varying operating conditions on the performance of the thermodynamic cycle has been analyzed. Operating pressure of the cycle has been optimized for thermal efficiency for each fluid at each source temperature. The condensation pressure is determined by the cooling condition and is kept fixed for each condensation temperature. Energy and exergy efficiencies of the cycle have been obtained for the pure fluids as a function of heat source temperature. Mixtures of organic fluids have been analyzed and effect of composition on performance of the thermodynamic cycle has been studied. It is observed that thermal efficiency over 20% can be achieved for 200°C heat source temperature and the lowest cooling temperature. When mixtures are considered as working fluids, the thermal efficiency of the cycle is observed to remain between the thermal efficiencies of the constituent fluids.Copyright

PERFORMANCE ANALYSIS OF A RANKINE-GOSWAMI COMBINED CYCLE

Improving the efficiency of thermodynamic cycles plays a fundamental role in reducing the cost of solar power plants. These plants work normally with Rankine cycles which present some disadvantages due to the thermodynamic behavior of steam at low pressures. These disadvantages can be reduced by introducing alternatives such as combined cycles which combine the best features of each cycle. In this paper a combined Rankine-Goswami cycle (RGC) is proposed and a thermodynamic analysis is conducted. The Goswami cycle, used as a bottoming cycle, uses ammonia-water mixture as the working fluid and produces power and refrigeration while power is the primary goal. This bottoming cycle, reduces the energy losses in the traditional condenser and eliminates the high specific volume and poor vapor quality presented in the last stages of the lower pressure turbine in the Rankine cycle. In addition, the use of absorption condensation in the Goswami cycle, for regeneration of the strong solution, allows operating the low pressure side of the cycle above atmospheric pressure which eliminates the need for maintaining a vacuum pressure in the condenser. The performance of the proposed combined Rankine-Goswami cycle, under full load, was investigated for applications in parabolic trough solar thermal plants for a range from 40 to 50 MW sizes. A sensitivity analysis to study the effect of the ammonia concentration, condenser pressure and rectifier concentration on the cycle efficiency, network and cooling was performed. The results indicate that the proposed RGC provide a difference in net power output between 15.7 and 42.3% for condenser pressures between 1 to 9 bars. The maximum effective first law and exergy efficiencies for an ammonia mass fraction of 0.5 are calculated as 36.7% and 24.7% respectively for the base case (no superheater or rectifier process).Copyright

Multi-Objective Optimization of a Combined Power and Cooling Cycle for Low-Grade and Mid-Grade Heat Sources

Optimization of thermodynamic cycles is important for the efficient utilization of energy sources; indeed it is more crucial for the cycles utilizing low grade heat sources where the cycle efficiencies are smaller compared to high temperature power cycles. This paper presents the optimization of a combined power/cooling cycle, also known as the Goswami Cycle, which combines the Rankine and absorption refrigeration cycles. The cycle uses a special binary fluid mixture as the working fluid and produces power and refrigeration. In this regard, multi-objective genetic algorithms (GA) are used for Pareto approach optimization of the thermodynamic cycle. The optimization study includes two cases. In the first case the performance of the cycle is evaluated as it is used as a bottoming cycle, and in the second case as it is used as a top cycle utilizing solar energy or geothermal sources. The important thermodynamic objectives that have been considered in this work are, namely, work output, cooling capacity, effective first law and exergy efficiencies. Optimization is carried out by varying the selected design variables; boiler temperature and pressure, rectifier temperature, and basic solution concentration. The boiler temperature is varied between 70–150 °C and 150–250 °C for the first and the second cases, respectively.Copyright