Parameter optimization of the biohydrogen real time power generating system using differential evolution algorithm

Huang, Sy‐Ruen; Wu, Chueh-Cheng; Lin, Chiu‐Yue; Chen, Hong-Tai

doi:10.1016/j.ijhydene.2010.04.038

Cited by 16 publications

(11 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…they may lack flexibility to handle arbitrary constraints, are sensitive to noise, may require function derivatives and are prone to remain trapped in local minima. [45,46] and Differential Evolution (DE) [47,48], just to name the most well known.…”

Section: Stochastic Optimizationmentioning

confidence: 99%

A selective hybrid stochastic strategy for fuel-cell multi-parameter identification

Guarnieri

Negro

Noto

2016

Journal of Power Sources

View full text Add to dashboard Cite

The in situ identification of fuel-cell material parameters is crucial both for guiding the research for advanced functionalized materials and for fitting multiphysics models, which can be used in fuel cell performance evaluation and optimization. However, this identification still remains challenging when dealing with direct measurements. This paper presents a method for achieving this aim by stochastic optimization. Such techniques have been applied to the analysis of fuel cells for ten years, but typically to specific problems and by means of semi-empirical models, with an increased number of articles published in the last years. We present an original formulation that makes use of an accurate zero-dimensional multi-physical model of a polymer electrolyte membrane fuel cell and of two cooperating stochastic algorithms, particle swarm optimization and differential evolution, to extract multiple material parameters (exchange current density, mass transfer coefficient, diffusivity, conductivity, activation barriers ...) from the experimental data of polarization curves (i.e. in situ measurements) under some controlled temperature, gas back pressure and humidification. The method is suitable for application in other fields where fitting of multiphysics nonlinear models is involved. © 2016 Elsevier B.V. All rights reserved

show abstract

Section: Stochastic Optimizationmentioning

confidence: 99%

A selective hybrid stochastic strategy for fuel-cell multi-parameter identification

Guarnieri

Negro

Noto

2016

Journal of Power Sources

View full text Add to dashboard Cite

show abstract

“…Na literatura pode-se encontrar diversas aplicações do algoritmo de ED, dentre quais pode-se citar: otimização de processos com controle ótimo (Souza et al, 2015), otimização de parâmetros da energia gerada em sistemas de biohidrogênio (HUANG et al, 2010), sintonia de controladores PID (SOUZA et al, 2011), determinação da difusividade térmica aparente na secagem das frutas (MARIANI et al, 2008), além de inúmeras aplicações em áreas distintas da ciência.…”

Section: Geração Do Candidato: C1[i] = P[i1] + F (P[i2]-p[i3])unclassified

Determinação De Funções De Transferência De Processos Químicos Através Do Método De Evolução Diferencial Utilizando O Scilab

Rezende¹,

Souza²

2015

Anais Do Congresso Brasileiro De Engenharia Química Em Iniciação Científica - Cobeq IC 2015

View full text Add to dashboard Cite

RESUMO -Existem diversos métodos de otimização e as particularidades do processo é que determina seu desempenho. Os métodos naturais de otimização têm sido amplamente estudados. O objetivo deste trabalho é apresentar um estudo do uso do método de otimização natural denominado Evolução Diferencial para a obtenção de Funções de Transferência que descrevem o comportamento de um processo.

show abstract

“…To conquer this drawback, metaheuristic algorithms such as Genetic Algorithms (GA) [20][21][22][23][24][25], Simulated Annealing (SA) [26,27], Differential Evolution (DE) [28,29], Particle Swarm Optimization (PSO) [30,31], Artificial Immune System (AIS) [5], Seeker Optimization Algorithm (SOA) [32], Harmony Search (HS) [33,34], Hybrid Artificial Bee Colony (HABC) [19], Artificial Bee Swarm Algorithm (ABSA) [35], P System Based Optimization (PSBO) [36], Teaching-learning-based optimization (TLBO) [37], Biogeography-based optimization [38] and Bird Mating Optimization (BMO) [39] have been applied in this problem. Metaheuristics generally do not need domain information and they are derivative free methods which perform stochastic movements to obtain global optimum point.…”

Section: Introductionmentioning

confidence: 99%

Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm

Turgut

Çoban

2016

Ain Shams Engineering Journal

110

View full text Add to dashboard Cite

Simulation proton exchange membrane fuel cell (PEMFC) performance accurately is a challenging process. Many mathematical models have been existed, yet due to lack of accurate parameter estimations, considerable errors might occur. Nowadays, meta-heuristic optimization algorithms have been successfully applied for parameter identification of PEMFC models. In this study, Teaching Learning Based Optimization method (TLBO) is hybridized with Differential Evolution (DE) algorithm for successful estimation of unknown PEMFC model parameters. Efficiency of the proposed algorithm is tested with several benchmark problems.

show abstract

Parameter optimization of the biohydrogen real time power generating system using differential evolution algorithm

Cited by 16 publications

References 9 publications

A selective hybrid stochastic strategy for fuel-cell multi-parameter identification

A selective hybrid stochastic strategy for fuel-cell multi-parameter identification

Determinação De Funções De Transferência De Processos Químicos Através Do Método De Evolução Diferencial Utilizando O Scilab

Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm

Contact Info

Product

Resources

About