Dynamic modeling of an integrated reformer membrane fuel cell system

Pravin, P S; Gudi, Ravindra D.; Bhartiya, Sharad

doi:10.1016/j.ifacol.2017.08.2341

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Fuel-cell systems are increasingly used in the fields of transport and energy production. This evolution motivated the scientific community to develop physical models that are widely used for simulation [14], control [7], and condition monitoring [9].…”

Section: Related Workmentioning

confidence: 99%

Failure Prognosis Based on Relevant Measurements Identification and Data-Driven Trend-Modeling: Application to a Fuel Cell System

et al. 2021

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Fuel cells are key elements in the transition to clean energy thanks to their neutral carbon footprint, as well as their great capacity for the generation of electrical energy by oxidizing hydrogen. However, these cells operate under straining conditions of temperature and humidity that favor degradation processes. Furthermore, the presence of hydrogen—a highly flammable gas—renders the assessment of their degradations and failures crucial to the safety of their use. This paper deals with the combination of physical knowledge and data analysis for the identification of health indices (HIs) that carry information on the degradation process of fuel cells. Then, a failure prognosis method is achieved through the trend modeling of the identified HI using a data-driven and updatable state model. Finally, the remaining useful life is predicted through the calculation of the times of crossing of the predicted HI and the failure threshold. The trend model is updated when the estimation error between the predicted and measured values of the HI surpasses a predefined threshold to guarantee the adaptation of the prediction to changes in the operating conditions of the system. The effectiveness of the proposed approach is demonstrated by evaluating the obtained experimental results with prognosis performance analysis techniques.

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Section: Related Workmentioning

confidence: 99%

Failure Prognosis Based on Relevant Measurements Identification and Data-Driven Trend-Modeling: Application to a Fuel Cell System

et al. 2021

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“…Hedayati et al [11] developed a dynamic model of an ethanol-reforming system within a catalytic membrane reactor, successfully validating it under different reactor pressure conditions and feed loads. Pravin et al conducted mathematical modelling and analysis of an integrated system comprising a reformer, a separation membrane, and a fuel cell [12], subsequently extending their research to predictive control following the addition of an auxiliary electric battery [13]. Marquez-Ruiz et al focused on the control of a catalytic membrane reactor for the steam reforming of methane [14], developing a nonlinear model of the system and testing various control strategies.…”

Section: Introductionmentioning

confidence: 99%

Experimental Control of a Methanol Catalytic Membrane Reformer

Cifuentes,

Serra,

Torres

et al. 2023

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A simple proportional integral (PI) controller with scheduled gain has been developed and implemented in a catalytic membrane reactor (CMR) to obtain pure hydrogen from a methanol steam reforming process. The controller is designed to track the setpoint of the pure hydrogen flow rate in the permeate side of the CMR via the manipulation of the fuel inlet flow rate. Therefore, the controller actuator is the liquid pump that provides the mixture of methanol and water to the reactor. Within the CMR, the catalytic pellets of PdZn/ZnAl2O4/Al2O3 have been used to facilitate the methanol steam-reforming reaction under stoichiometric conditions (S/C = 1), and Pd–Ag metallic membranes have been employed to simultaneously separate the generated hydrogen. The PI controller design is based on a mathematical model constructed using transfer functions acquired from dynamic experiments conducted with the CMR. The controller has been successfully implemented, and experimental validation tests have been carried out at 450 °C and relative pressures of 6, 8, 10, and 12 bar.

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“…However, H+SOFCs require pure hydrogen to the exclusion of other more readily available fuels [19,20], but they can work in fully reversible mode [21]. Theoretically, methane steam reforming processes [22][23][24][25][26] can take place at H+SOFC, as in the classical oxygen ion-conducting SOFC. However, there is an issue with the delivery of steam.…”

Section: Introductionmentioning

confidence: 99%

Key Parameters of Proton‐conducting Solid Oxide Fuel Cells from the Perspective of Coherence with Models

et al. 2020

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The paper presents a review of thermal, flow, and material parameters from the perspective of modeling proton‐conducting solid oxide fuel cells (H+SOFC). Comments are offered regarding key parameters selected through research of the literature, featuring mostly material‐related properties of electrolyte and electrodes. Some general relationships are proposed here for the purpose of modeling H+SOFC. The most important parameter is fuel cell operational temperature. Next to the temperature, partial pressures of hydrogen on both sides of H+SOFC impact mainly maximum voltage. The materials from which the fuel cell is built do not directly affect the operation of the fuel cell in the sense that they can be taken as fixed values. Indirectly, their influence can be seen in the resistances, that electrolyte conducts for proton flux and electrodes for electrons. In some cases, double ion/electron conductivity may occur.

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Dynamic modeling of an integrated reformer membrane fuel cell system

Cited by 3 publications

References 10 publications

Failure Prognosis Based on Relevant Measurements Identification and Data-Driven Trend-Modeling: Application to a Fuel Cell System

Failure Prognosis Based on Relevant Measurements Identification and Data-Driven Trend-Modeling: Application to a Fuel Cell System

Experimental Control of a Methanol Catalytic Membrane Reformer

Key Parameters of Proton‐conducting Solid Oxide Fuel Cells from the Perspective of Coherence with Models

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