Analysis of the dynamic performance of a microbial fuel cell using a system identification approach

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Analysis of the dynamic performance of a microbial fuel cell using a system identification approach. / Boghani, Hitesh C.; Kim, Jung Rae; Dinsdale, Richard M.; Guwy, Alan J.; Premier, Giuliano C.

In: Journal of Power Sources, Vol. 238, 22.04.2013, p. 218-226.

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Boghani, Hitesh C. ; Kim, Jung Rae ; Dinsdale, Richard M. ; Guwy, Alan J. ; Premier, Giuliano C. / Analysis of the dynamic performance of a microbial fuel cell using a system identification approach. In: Journal of Power Sources. 2013 ; Vol. 238. pp. 218-226.

Bibtex

@article{161c14175f8a49c4a333b25da447b4a6,
title = "Analysis of the dynamic performance of a microbial fuel cell using a system identification approach",
abstract = "Microbial fuel cells (MFCs) are bioelectrochemical devices which use micro-organisms as catalyst for electrogenesis at the anode; oxidizing biodegradable substrate to produce electrical current. MFC power output is a function of many factors; including pH, temperature, loading rate, flow rate and electrical load. The study presents a system identification approach to determine a set of linear dynamic black box models able to quantify and represent specific nonlinear characteristics of a MFC. A sandwich-type MFC was subjected to varying electrical loads of various pseudo-random and step inputs, while observing the MFC voltage. Nonlinear behaviour was inferred from assumed piecewise linearised first order dynamic responses, at different operating points. The time constants increased from 0.5 s with PRBS loading of 100-150, to 6.2 s at 950-1 k; although steady state gain varied little, (0.12-0.20 mV -1). This suggests that the MFC's non-linear behaviour, dependent on operating conditions, may be adequately represented by a series of linear models. System identification suggested that linear 4th order ARX models produce the best fit. However, reasonable prediction was observed using piecewise linearised first order models. The models could be used to design and optimize controllers to regulate power and/or voltage generation.",
keywords = "Bioelectrochemical system (BES), Microbial fuel cell (MFC), Nonlinear system, Parametric modelling, Piece-wise linearization, System identification",
author = "Boghani, {Hitesh C.} and Kim, {Jung Rae} and Dinsdale, {Richard M.} and Guwy, {Alan J.} and Premier, {Giuliano C.}",
year = "2013",
month = apr,
day = "22",
doi = "10.1016/j.jpowsour.2013.03.061",
language = "English",
volume = "238",
pages = "218--226",
journal = "Journal of Power Sources",
issn = "0378-7753",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Analysis of the dynamic performance of a microbial fuel cell using a system identification approach

AU - Boghani, Hitesh C.

AU - Kim, Jung Rae

AU - Dinsdale, Richard M.

AU - Guwy, Alan J.

AU - Premier, Giuliano C.

PY - 2013/4/22

Y1 - 2013/4/22

N2 - Microbial fuel cells (MFCs) are bioelectrochemical devices which use micro-organisms as catalyst for electrogenesis at the anode; oxidizing biodegradable substrate to produce electrical current. MFC power output is a function of many factors; including pH, temperature, loading rate, flow rate and electrical load. The study presents a system identification approach to determine a set of linear dynamic black box models able to quantify and represent specific nonlinear characteristics of a MFC. A sandwich-type MFC was subjected to varying electrical loads of various pseudo-random and step inputs, while observing the MFC voltage. Nonlinear behaviour was inferred from assumed piecewise linearised first order dynamic responses, at different operating points. The time constants increased from 0.5 s with PRBS loading of 100-150, to 6.2 s at 950-1 k; although steady state gain varied little, (0.12-0.20 mV -1). This suggests that the MFC's non-linear behaviour, dependent on operating conditions, may be adequately represented by a series of linear models. System identification suggested that linear 4th order ARX models produce the best fit. However, reasonable prediction was observed using piecewise linearised first order models. The models could be used to design and optimize controllers to regulate power and/or voltage generation.

AB - Microbial fuel cells (MFCs) are bioelectrochemical devices which use micro-organisms as catalyst for electrogenesis at the anode; oxidizing biodegradable substrate to produce electrical current. MFC power output is a function of many factors; including pH, temperature, loading rate, flow rate and electrical load. The study presents a system identification approach to determine a set of linear dynamic black box models able to quantify and represent specific nonlinear characteristics of a MFC. A sandwich-type MFC was subjected to varying electrical loads of various pseudo-random and step inputs, while observing the MFC voltage. Nonlinear behaviour was inferred from assumed piecewise linearised first order dynamic responses, at different operating points. The time constants increased from 0.5 s with PRBS loading of 100-150, to 6.2 s at 950-1 k; although steady state gain varied little, (0.12-0.20 mV -1). This suggests that the MFC's non-linear behaviour, dependent on operating conditions, may be adequately represented by a series of linear models. System identification suggested that linear 4th order ARX models produce the best fit. However, reasonable prediction was observed using piecewise linearised first order models. The models could be used to design and optimize controllers to regulate power and/or voltage generation.

KW - Bioelectrochemical system (BES)

KW - Microbial fuel cell (MFC)

KW - Nonlinear system

KW - Parametric modelling

KW - Piece-wise linearization

KW - System identification

UR - http://www.scopus.com/inward/record.url?scp=84876264803&partnerID=8YFLogxK

U2 - 10.1016/j.jpowsour.2013.03.061

DO - 10.1016/j.jpowsour.2013.03.061

M3 - Article

AN - SCOPUS:84876264803

VL - 238

SP - 218

EP - 226

JO - Journal of Power Sources

JF - Journal of Power Sources

SN - 0378-7753

ER -