BEGIN:VCALENDAR
VERSION:2.0
CALSCALE:GREGORIAN
PRODID:iCalendar-Ruby
BEGIN:VEVENT
CATEGORIES:Colloquium/Seminar
DESCRIPTION:Harry Dudley\, Department of Applied Mathematics\, University o
 f Colorado Boulder\n\nDifferential-Algebraic Equation Models of Microbial E
 lectrolysis Cells\n\nMicrobial electrolysis cells (MECs) are an emerging te
 chnology that employs microorganisms to recover energy from organic waste i
 n the form of hydrogen. MECs consist of two types of microbes.  In these de
 vices\, bacteria on the electroactive anode\nbiofilm oxidize (in)organic su
 bstrate and transfer electrons to generate electrical current and release p
 rotons (H+). A small voltage (0.2−0.8 V) is needed to overcome the thermody
 namic barrier\, which is much lower than traditional water electrolysis (1.
 8–3.5 V). Hydrogen is produced via a reduction reaction as protons in solut
 ion react with electrons at the cathode. Methanogenic archaea reduce the ef
 ficiency of the system by consuming the substrate to make methane.  The out
 come of competition between electroactive\nbacteria and methanogens determi
 nes current and hydrogen production. \n\nIn this talk\, we discuss previous
  work on sensitivity and bifurcation analysis of differential-algebraic equ
 ation (DAE) models of microbial electrolysis\, current work on global stabi
 lity analysis using LaSalle’s invariance principle\, and provide a basis fo
 r future work on model selection with numerical analysis of the effect of v
 arious solvers. In different models\, either Sotomayor’s theorem for transc
 ritical bifurcations or LaSalle’s invariance principle can be used to deter
 mine when the stable equilibria exhibits competitive exclusion or coexisten
 ce with the outcome determined by particular model parameters.  We also pro
 pose a framework for investigating novel MEC models that could be used to b
 etter represent current and hydrogen production. A statistical model for in
 ter- and intra-substrate variability will be used to account for difference
 s in the current density data within and between substrates. Assumptions on
  distributions for the random effects will be examined and modified appropr
 iately to best represent MEC current data. A model selection criterion will
  be used to rank relative information loss among proposed MEC models. Since
  model selection criteria involve approximations of the maximum likelihood\
 , we will evaluate of the effects of numerical schemes on the discrepancy b
 etween model and data. This framework will be used to further development o
 f MEC modeling.
DTEND:20181106T220000Z
DTSTAMP:20260314T045457Z
DTSTART:20181106T210000Z
GEO:40.006791;-105.262818
LOCATION:Engineering Center\, ECCR 257
SEQUENCE:0
SUMMARY:Mathematical Biology Seminar - Harry Dudley
UID:tag:localist.com\,2008:EventInstance_4061914
URL:https://calendar.colorado.edu/event/mathematical_biology_seminar_-_harr
 y_dudley_9910
END:VEVENT
END:VCALENDAR