Optimization of a Solid Oxide Fuel Cell and Gas Turbine Hybrid System

2015 ◽  
Author(s):  
Setthawut Kanarit ◽  
Wirinya Karunkeyoon ◽  
Ali Al-Alili ◽  
Valerie Eveloy ◽  
Peter Rodgers
Keyword(s):  
Hybrid System ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Design Variables ◽  
Parametric Studies

The low efficiency and high environmental impact of conventional power cycles are a major concern. The integration of solid oxide fuel cells (SOFCs) and gas turbines (GTs) has been proposed in the literature to increase fuel to power conversion. Parametric studies are usually conducted to identify suitable operating conditions for such integrated systems. However, parametric studies only consider the main effects of the design variables and do not provide information on the interactive effect of the design variables. In this study, a multi-objective optimization is performed to optimize the performance of a SOFC-GT hybrid system. The objective functions are system efficiency and total cost rate, including capital, operating, and environmental penalty costs. The design variables are selected based on sensitivity analysis to assess the effect of the variables on the objective functions. The performance of the SOFC-GT hybrid system is modeled in Aspen Plus, while MATLAB is used for multi-objective optimization. The multi-objective optimization solution is presented in terms of a Pareto frontier.

Performance Study of Hybrid Solid Oxide Fuel Cell–Gas Turbine Power System

2010 ◽  
Vol 171-172 ◽  
pp. 319-322
Author(s):  
Hong Bin Zhao ◽  
Xu Liu
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Gas Turbine ◽  
Hybrid System ◽  
Cell Model ◽  
Research Work ◽  
Atmospheric Condition ◽  
Operating Conditions ◽  
Solid Oxide ◽  
Oxide Fuel

The simulation and analyses of a “bottoming cycle” solid oxide fuel cell–gas turbine (SOFC–GT) hybrid system at the standard atmospheric condition is presented in this paper. The fuel cell model used in this research work is based on a tubular Siemens–Westinghouse–type SOFC with 1.8MW capacity. Energy and exergy analyses of the whole system at fixed conditions are carried out. Then, comparisons of the exergy destruction and exergy efficiency of each component are also conducted to determine the potential capability of the hybrid system to generate power. Moreover, the effects of operating conditions including fuel flow rate and SOFC operating temperature on performances of the hybrid system are analyzed.

Modeling, parametric analysis and optimization of an anode-supported planar solid oxide fuel cell

2015 ◽  
Vol 229 (17) ◽  
pp. 3125-3140 ◽  
Author(s):  
Mehdi Borji ◽  
Kazem Atashkari ◽  
Nader Nariman-zadeh ◽  
Mehdi Masoumpour
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Temperature Difference ◽  
Current Density ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Multi Objective Optimization ◽  
Average Current Density ◽  
Multi Objective ◽  
Average Current

Solid oxide fuel cell is a promising tool for distributed power generation systems. This type of power system will experience different conditions during its operating life. The present study aims to simulate mathematically a direct internal reforming planar type anode supported solid oxide fuel cell considering mass and energy conservation equations along with a complete electrochemical model. Two main reactions, namely water–gas shift reaction and methane steam reforming reaction, are considered as two dominant reactions occurring in a fuel cell. Such a model may be employed to examine the effect of different operating conditions on main solid oxide fuel cell parameters, such as temperature gradients, power, and efficiency. Furthermore, using such mathematical model, a multi-objective optimization procedure can be applied to determine maximum cell efficiency and output power under constraints such as the allowable temperature difference and limited operating potential. The selected design variables are air ratio, fuel utilization, average current density, steam to carbon ratio, and pre-reforming rate of methane. It has been revealed that any increase in pre-reforming rate of methane and steam to carbon ratio of the entering fuel will lead to efficiency penalty and more uniform temperature distribution along the cell. In addition, the more average current density increases, the less electric efficiency is achieved, and on the other hand, the more temperature difference along the cell is seen. Besides, it is shown that some interesting and important relationships as useful optimal design principles involved in the performance of solid oxide fuel cells can be discovered by Pareto based multi-objective optimization of the mathematically obtained model representing their electric performance. Such important optimal principles would not have been obtained without the use of both mathematical modeling and the Pareto optimization approach.

scholarly journals Multi-Objective Optimization of Solid Oxide Fuel Cell/GT Combined Heat and Power System: A comparison between Particle Swarm and Genetic Algorithms

2020 ◽  
Author(s):  
Taher Hajilounezhad ◽  
Sadegh Safari ◽  
Mehdi Aliehyaei
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Computational Effort ◽  
Solid Oxide ◽  
Maximum Power ◽  
Oxide Fuel ◽  
Multi Objective Optimization ◽  
Multi Objective

Many studies have attempted to optimize integrated Solid Oxide Fuel Cell-Gas Turbine (SOFC-GT), although different and somehow conflicting results are reported employing various algorithms. In this study, Multi-Objective Optimization (MOO) is employed to approach the optimal design of SOFC-GT considering all prevailing factors. The emphasis is placed on the evaluation of the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) performance as two effective approaches for solving the multi-objective and non-linear optimization problems. Multi- objective optimization is carried out on two vital objectives; the electrical efficiency and the overall output power of the system. The considerable achievements are the set of optimal points that aim to identify the system optimal performance which provides a practical basis for the decision-makers to choose the appropriate target functions. For the studied conditions, the two algorithms nearly exhibit similar performance, while the PSO is faster and more efficient in terms of computational effort. The PSO appears to achieve its ultimate parameter values in fewer generations compared to the GA algorithm under the examined circumstances. It is found that the maximum power of 410 kW is accomplished employing the GA optimization method with an efficiency of 64%, while PSO method yields the maximum power of 419.19 kW at the efficiency of 58.9%. The results stress that PSO offers more satisfactory convergence and fidelity of the solution for the SOFC-GT MOO problems.

Robustness Against Large Variations in Multi-Objective Optimization Problems

2013 ◽  
Author(s):  
Weijun Wang ◽  
Stéphane Caro ◽  
Fouad Bennis
Keyword(s):  
Optimization Problems ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Design Environment ◽  
Multi Objective ◽  
Uncertainty Sources ◽  
Design Variables ◽  
Robustness Index ◽  
Environment Parameters

In the presence of multiple optimal solutions in multi-modal optimization problems and in multi-objective optimization problems, the designer may be interested in the robustness of those solutions to make a decision. Here, the robustness is related to the sensitivity of the performance functions to uncertainties. The uncertainty sources include the uncertainties in the design variables, in the design environment parameters, in the model of objective functions and in the designer’s preference. There exist many robustness indices in the literature that deal with small variations in the design variables and design environment parameters, but few robustness indices consider large variations. In this paper, a new robustness index is introduced to deal with large variations in the design environment parameters. The proposed index is bounded between zero and one, and measures the probability of a solution to be optimal with respect to the values of the design environment parameters. The larger the robustness index, the more robust the solution with regard to large variations in the design environment parameters. Finally, two illustrative examples are given to highlight the contributions of this paper.

scholarly journals Thermodynamic assessment and performance optimization of solid oxide fuel cell-Stirling heat engine–reverse osmosis desalination

2020 ◽  
Author(s):  
Omolbanin Shakouri ◽  
Mohammad Hossein Ahmadi ◽  
Mahmood Farzaneh Gord
Keyword(s):  
High Temperature ◽  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Hybrid System ◽  
Exergy Efficiency ◽  
Stirling Engine ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Exergy Destruction ◽  
Objective Functions

Abstract Fuel cells are chemical energy converted to electric energy, which is today a new technology in energy production. Among the existing fuel cells, solid fuel oxide cells have a high potential for use in synthetic and combined production systems due to their high temperature (700–1000°C). The solid oxide fuel cell (SOFC) output acts as a high-temperature source, which can be used for heat engines such as the Stirling engine as a high-temperature heat source. A hybrid system including solid oxide fuel cell and Stirling engine and reverse osmosis desalinating is a cogeneration plant. This system includes two parts for power generation; the first part is power generated in the SOFC, and the second part is that with use of heat rejection of solid oxide fuel cell to generate power in the Stirling engine. Also, due to the water critical situation in the world and the need for freshwater, it is very common to use desalination systems. In this study, important goals such as power density and exergy destruction, and exergy efficiency, have been investigated. In general, the performance of the hybrid system has been investigated. Firstly, a thermodynamic analysis for all components of the system and then multi-objective optimization performed for several objective functions include exergy destruction density, exergy efficiency, fuel cell power and freshwater production rate. The present optimization is performed for two overall purposes; the first purpose is to improve fuel cell output power, exergy efficiency and exergy destruction density, and the second purpose is to improve the exergy efficiency, the amount of freshwater production and exergy destruction density. In this optimization, three robust decision-making methods TOPSIS, LINMAP and FUZZY are used. Two scenarios are presented; the first scenario is covering power, exergy efficiency and exergy destruction density. The output power and exergy efficiency, and exergy destruction density, have optimum values in the TOPSIS method’s results. The values are 939.393 (kW), 0.838 and 1139.85 (w/m2) respectively. In the second scenario that includes the freshwater production rate, the exergy destruction density and exergy efficiency, three objective functions are at their peak in the FUZZY results, which are 5.697 (kg/s), 7561.192 (w/m2) and 0.7421 respectively.

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