Thermoeconomic analysis and multi‐objective optimization of a solid‐oxide fuel cell plant coupled with methane tri‐reforming: Effects of thermochemical recuperation

2021 ◽  
Author(s):  
Mohsen Sadeghi ◽  
Moharram Jafari ◽  
S. M. S. Mahmoudi ◽  
Mortaza Yari ◽  
Marc A. Rosen
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Multi Objective Optimization ◽  
Multi Objective

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.

Multi-objective optimization of sorption enhanced steam biomass gasification with solid oxide fuel cell

2019 ◽  
Vol 182 ◽  
pp. 412-429 ◽  
Author(s):  
Thanaphorn Detchusananard ◽  
Shivom Sharma ◽  
François Maréchal ◽  
Amornchai Arpornwichanop
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Biomass Gasification ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Robust Multi-Objective Optimization of Solid Oxide Fuel Cell–Gas Turbine Hybrid Cycle and Uncertainty Analysis

2018 ◽  
Vol 15 (4) ◽  
Author(s):  
Shivom Sharma ◽  
François Maréchal
Keyword(s):  
Fuel Cell ◽  
Uncertainty Analysis ◽  
Solid Oxide Fuel Cell ◽  
Gas Turbine ◽  
Environmental Impacts ◽  
Capital Cost ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Multi Objective Optimization ◽  
Multi Objective

Chemical process optimization problems often have multiple and conflicting objectives, such as capital cost, operating cost, production cost, profit, energy consumptions, and environmental impacts. In such cases, multi-objective optimization (MOO) is suitable in finding many Pareto optimal solutions, to understand the quantitative tradeoffs among the objectives, and also to obtain the optimal values of decision variables. Gaseous fuel can be converted into heat, power, and electricity, using combustion engine, gas turbine (GT), or solid oxide fuel cell (SOFC). Of these, SOFC with GT has shown higher thermodynamic performance. This hybrid conversion system leads to a better utilization of natural resource, reduced environmental impacts, and more profit. This study optimizes performance of SOFC–GT system for maximization of annual profit and minimization of annualized capital cost, simultaneously. For optimal SOFC–GT designs, the composite curves for maximum amount of possible heat recovery indicate good performance of the hybrid system. Further, first law energy and exergy efficiencies of optimal SOFC–GT designs are significantly better compared to traditional conversion systems. In order to obtain flexible design in the presence of uncertain parameters, robust MOO of SOFC–GT system was also performed. Finally, Pareto solutions obtained via normal and robust MOO approaches are considered for parametric uncertainty analysis with respect to market and operating conditions, and solution obtained via robust MOO found to be less sensitive.

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