Modeling solid oxide fuel cells using continuous-time recurrent fuzzy systems

Flemming, Anna; Adamy, Jürgen

doi:10.1016/j.engappai.2008.02.006

Cited by 12 publications

(2 citation statements)

References 25 publications

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“…Fuel cells present an energy source that is independent of fossil fuels. They are environmentally friendly while meeting sustainability goals . Among the various types of fuel cells, proton exchange membrane fuel cell (PEMFC) seems to be more attractive because of its simplicity, viability, quick start up, prompt response time, higher power density, and long cycle life .…”

Section: Introductionmentioning

confidence: 99%

Adaptive neuro-fuzzy inference system and artificial neural network modeling of proton exchange membrane fuel cells based on nanocomposite and recast Nafion membranes

Amirinejad

Tavajohi-Hasankiadeh

Madaeni

et al. 2011

Int. J. Energy Res.

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SUMMARY In this study, a proton exchange membrane fuel cell (PEMFC) is modeled by multilayer perceptron neural network (MLPNN), RBF neural network (RBFNN), and adaptive neuro‐fuzzy inference system (ANFIS). Experimental data are obtained on the basis of the fabricated membrane‐electrode assembly (MEA) responses using prepared nanocomposite and recast Nafion membranes in the PEMFC. Four parameters including cell temperature, inlet gas temperature, current density, and inorganic additive percent are used as inputs, and the cell voltage is considered as the output. The results show that there is no considerable discrepancy between the RBFNN accuracy (R = 0.99554) and the MLPNN accuracy (R = 0.99609) for the performance prediction. The required time for developing the RBFNN model is significantly lower than the MLPNN model. A variety of ANFIS structure is explored to approximate the behavior of the system. The effect of cell and inlet gas temperatures on the PEMFC performance is investigated by the ANFIS developed model. Predicted polarization and power–current behavior by the ANFIS for the MEA prepared by the recast Nafion and the nanocomposite membranes at the cell temperatures 50 °C to110°C are in high agreement with the experimental data. Predicted data by the ANFIS show that because of the property of Cs2.5H0.5PW12O40 additive for retaining water, much higher current density and power density at the same voltage are achieved for the nanocomposite membrane compared with the recast Nafion membrane in the PEMFC. Copyright © 2011 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Adaptive neuro-fuzzy inference system and artificial neural network modeling of proton exchange membrane fuel cells based on nanocomposite and recast Nafion membranes

Amirinejad

Tavajohi-Hasankiadeh

Madaeni

et al. 2011

Int. J. Energy Res.

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“…In [2] werden RFS dann auf den Fall zeitkontinuierlicher Größen erweitert und systemtheoretische Eigenschaften wie Ruhelagen und Stabilität untersucht. In [5] …”

Section: Introductionunclassified

Modellierung dynamischer Systeme mit zeitkontinuierlichen rekurrenten Fuzzy-SystemenModeling of Dynamical Systems Using Continuous-time Recurrent Fuzzy Systems

Adamy¹,

Schwung

2009

At - Automatisierungstechnik

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In diesem Beitrag wird die Anwendung zeitkontinuierlicher rekurrenter Fuzzy-Systeme (KRFS) zur Modellbildung technischer Systeme beschrieben. Ausgehend von der Systembeschreibung rekurrenter Fuzzy-Systeme werden Verfahren vorgestellt, mit denen das KRFS das Verhalten des Prozesses anhand von Messdaten und qualitativem Prozesswissen erlernen kann. Die Leistungsfähigkeit wird anhand der Modellierung eines biotechnologischen Prozesses gezeigt.This paper presents the application of continuous-time recurrent fuzzy systems for modeling of dynamical systems. After a short introduction on recurrent fuzzy systems, we present methods for learning the dynamics using both measurement data and qualitative knowledge. The capability of the approach is shown by modeling of a biotechnological process.Schlagwörter: Zeitkontinuierliche rekurrente Fuzzy Systeme, qualitative Modellierung dynamischer Systeme, Lernverfahren

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Readiness of artificial intelligence technology for managing energy demands from renewable sources

Verma,

Sandys,

Matthews

et al. 2024

Engineering Applications of Artificial Intelligence

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Modeling solid oxide fuel cells using continuous-time recurrent fuzzy systems

Cited by 12 publications

References 25 publications

Adaptive neuro-fuzzy inference system and artificial neural network modeling of proton exchange membrane fuel cells based on nanocomposite and recast Nafion membranes

Adaptive neuro-fuzzy inference system and artificial neural network modeling of proton exchange membrane fuel cells based on nanocomposite and recast Nafion membranes

Modellierung dynamischer Systeme mit zeitkontinuierlichen rekurrenten Fuzzy-SystemenModeling of Dynamical Systems Using Continuous-time Recurrent Fuzzy Systems

Readiness of artificial intelligence technology for managing energy demands from renewable sources

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