A carbon monoxide sensor in polymer electrolyte fuel cells based on symbolic dynamic filtering

K. S. Bhambare, S. Gupta, M. M. Mench, A. Ray

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Carbon monoxide (CO) dramatically reduces the performance of a fuel cell stack if not remediated. Remediation generally requires parasitic bleeding of a small fraction (<5%) of air into the fuel stream to promote oxidation of the CO and use of a platinum-ruthenium or other noble metal based catalyst. For enhancement of system efficiency, air bleed should be controlled using real-time feedback of CO level in the feed-stream. In this paper, a recently reported data-driven pattern identification method, called Symbolic Dynamic Filtering (SDF), is applied for on-line sensing of CO content in an impure reformed hydrogen fuel stream. A small fuel cell, fuelled by a diverted stream of reformate, is used as a CO sensor. CO level is determined through time series analysis of the dynamic current response of the sensor cell due to load oscillations. The pattern identification algorithms are built upon the underlying concepts of Symbolic Dynamics, Information Theory and Probabilistic Finite State Machines. The effect of temperature on sensitivity was analyzed, and results demonstrate the efficacy of CO sensor under different operating conditions. The sensitivity of the CO sensor can be tailored for a particular application by changing the type of catalyst, its loading and operation temperature. A similar approach is now being used to develop online sensors for a variety of other important fuel cell phenomena, such as flooding and catalyst degradation.

Original languageEnglish (US)
Pages (from-to)803-815
Number of pages13
JournalSensors and Actuators, B: Chemical
Volume134
Issue number2
DOIs
StatePublished - Sep 25 2008

Fingerprint

Carbon Monoxide
Carbon monoxide
carbon monoxide
Electrolytes
fuel cells
Fuel cells
Polymers
electrolytes
sensors
Sensors
polymers
catalysts
Catalysts
Turing machines
hydrogen fuels
bleeding
time series analysis
Hydrogen fuels
Time series analysis
Ruthenium

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Condensed Matter Physics
  • Surfaces, Coatings and Films
  • Metals and Alloys
  • Electrical and Electronic Engineering
  • Materials Chemistry

Cite this

@article{627ba82935694e369ae4187a095401fc,
title = "A carbon monoxide sensor in polymer electrolyte fuel cells based on symbolic dynamic filtering",
abstract = "Carbon monoxide (CO) dramatically reduces the performance of a fuel cell stack if not remediated. Remediation generally requires parasitic bleeding of a small fraction (<5{\%}) of air into the fuel stream to promote oxidation of the CO and use of a platinum-ruthenium or other noble metal based catalyst. For enhancement of system efficiency, air bleed should be controlled using real-time feedback of CO level in the feed-stream. In this paper, a recently reported data-driven pattern identification method, called Symbolic Dynamic Filtering (SDF), is applied for on-line sensing of CO content in an impure reformed hydrogen fuel stream. A small fuel cell, fuelled by a diverted stream of reformate, is used as a CO sensor. CO level is determined through time series analysis of the dynamic current response of the sensor cell due to load oscillations. The pattern identification algorithms are built upon the underlying concepts of Symbolic Dynamics, Information Theory and Probabilistic Finite State Machines. The effect of temperature on sensitivity was analyzed, and results demonstrate the efficacy of CO sensor under different operating conditions. The sensitivity of the CO sensor can be tailored for a particular application by changing the type of catalyst, its loading and operation temperature. A similar approach is now being used to develop online sensors for a variety of other important fuel cell phenomena, such as flooding and catalyst degradation.",
author = "Bhambare, {K. S.} and S. Gupta and Mench, {M. M.} and A. Ray",
year = "2008",
month = "9",
day = "25",
doi = "10.1016/j.snb.2008.06.057",
language = "English (US)",
volume = "134",
pages = "803--815",
journal = "Sensors and Actuators, B: Chemical",
issn = "0925-4005",
publisher = "Elsevier",
number = "2",

}

A carbon monoxide sensor in polymer electrolyte fuel cells based on symbolic dynamic filtering. / Bhambare, K. S.; Gupta, S.; Mench, M. M.; Ray, A.

In: Sensors and Actuators, B: Chemical, Vol. 134, No. 2, 25.09.2008, p. 803-815.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A carbon monoxide sensor in polymer electrolyte fuel cells based on symbolic dynamic filtering

AU - Bhambare, K. S.

AU - Gupta, S.

AU - Mench, M. M.

AU - Ray, A.

PY - 2008/9/25

Y1 - 2008/9/25

N2 - Carbon monoxide (CO) dramatically reduces the performance of a fuel cell stack if not remediated. Remediation generally requires parasitic bleeding of a small fraction (<5%) of air into the fuel stream to promote oxidation of the CO and use of a platinum-ruthenium or other noble metal based catalyst. For enhancement of system efficiency, air bleed should be controlled using real-time feedback of CO level in the feed-stream. In this paper, a recently reported data-driven pattern identification method, called Symbolic Dynamic Filtering (SDF), is applied for on-line sensing of CO content in an impure reformed hydrogen fuel stream. A small fuel cell, fuelled by a diverted stream of reformate, is used as a CO sensor. CO level is determined through time series analysis of the dynamic current response of the sensor cell due to load oscillations. The pattern identification algorithms are built upon the underlying concepts of Symbolic Dynamics, Information Theory and Probabilistic Finite State Machines. The effect of temperature on sensitivity was analyzed, and results demonstrate the efficacy of CO sensor under different operating conditions. The sensitivity of the CO sensor can be tailored for a particular application by changing the type of catalyst, its loading and operation temperature. A similar approach is now being used to develop online sensors for a variety of other important fuel cell phenomena, such as flooding and catalyst degradation.

AB - Carbon monoxide (CO) dramatically reduces the performance of a fuel cell stack if not remediated. Remediation generally requires parasitic bleeding of a small fraction (<5%) of air into the fuel stream to promote oxidation of the CO and use of a platinum-ruthenium or other noble metal based catalyst. For enhancement of system efficiency, air bleed should be controlled using real-time feedback of CO level in the feed-stream. In this paper, a recently reported data-driven pattern identification method, called Symbolic Dynamic Filtering (SDF), is applied for on-line sensing of CO content in an impure reformed hydrogen fuel stream. A small fuel cell, fuelled by a diverted stream of reformate, is used as a CO sensor. CO level is determined through time series analysis of the dynamic current response of the sensor cell due to load oscillations. The pattern identification algorithms are built upon the underlying concepts of Symbolic Dynamics, Information Theory and Probabilistic Finite State Machines. The effect of temperature on sensitivity was analyzed, and results demonstrate the efficacy of CO sensor under different operating conditions. The sensitivity of the CO sensor can be tailored for a particular application by changing the type of catalyst, its loading and operation temperature. A similar approach is now being used to develop online sensors for a variety of other important fuel cell phenomena, such as flooding and catalyst degradation.

UR - http://www.scopus.com/inward/record.url?scp=51649116549&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=51649116549&partnerID=8YFLogxK

U2 - 10.1016/j.snb.2008.06.057

DO - 10.1016/j.snb.2008.06.057

M3 - Article

AN - SCOPUS:51649116549

VL - 134

SP - 803

EP - 815

JO - Sensors and Actuators, B: Chemical

JF - Sensors and Actuators, B: Chemical

SN - 0925-4005

IS - 2

ER -