Reactive Transport Modeling – Column Tests –

OXIRED II

Jana Nicolai, Harald Kalka – Mar 2011Umwelt- und Ingenieurtechnik GmbH Dresden

Project financed by:

Hydraulics1

Ion Exchange2

Redox Reactions4

Mineral Phases3

increasing complexity

Geometry & Hydraulics1

Experimental Data (TU Berlin)

Porosity

Dispersion

CEC

dried at 200°C

dried at 200°C

500 to 600 °C

Col 3

Col 4

Col 5

yes

yes

no

sediment

Fe coated sand

hydraulics

no

yes

yes

µ biology

naturalCol 2 yes no

Col 1 provisional test

inflo

w Q

Fe-coated

sand

35 cm

Q = 1.0 mL / min

porosity: = 0.35

dispersion: αL = 0.003 m

Column Setup (35 cells)

pure sand

Bromide breakthrough

35 cm

Column Setup (35 cells)

pure sand

Col 1Col 2Col 3

Col 4

Col 5

Fe-coatedsand

mono-layer

anaerob.zone

aerob.zone

0

5

10

15

20

25

30

35

40

45

5 6 7 8 9 10 11time [days]

Bro

mid

e [m

g/L]

pure ADV

DISP = 0.3 cm DISP = 0.6 cm

DISP = 1.2 cm

Longitudinal Dispersion

Note: There is no numerical dispersion (blue curve).

Bromide

Col 3

Ion Exchange2

without Ion Exchange

Li

Br

Hydraulics (Retardation of Li)

Li

Br

with Ion Exchange

Col 2

scre

en sh

ots

of T

RN

Q = 1.3 mL/min

Fe-coated / dried sediment 200°C

Fe-coated / 500 to 600 °C

Br Li

Br

Br

Li

Li

Li

Col 3

Col 4

Col 5

retarded ?adsorbed ?

Li

CTOT = 5 meq/L

dried sediment 200°C

Q = 1.0 mL/min

Q = 1.1 mL/min

CTOT = 20 meq/L

Col 1 Col 2 Col 3 Col 4 Col 5

flow Q mL / min provi-sional

1.3 1.3 1.0 1.1

time T days 14 11 19 13

sediment natural natural 200 °C 200 °C 550 °C

CTOT meq / L 20 20 20 20 5

µ biology yes yes yes yes (no)

Fe coated sand no no no yes yes

shrinks due to 550 °C

Column Parameters

other0%

NaX2%

KX1%

HX0%

MgX212%

CaX285%

HXKXNaXCaX2

MgX2other

Ion Exchange

time [days]

Mg [mg/L]

Ca [mg/L]

K [mg/L]

CEC = 20 mmol/L

Cation Exchange

H+ + X- = HX log K = 1.0 K+ + X- = KX log K = 0.7

Na+ + X- = NaX log K = 0.0 Li+ + X- = LiX log K = -0.08

Ca+2 + 2X- = CaX2 log K = 0.8 Mg+2 + 2X- = MgX2 log K = 0.6 NH4

+ + X- = NH4X log K = 0.6 Fe+2 + 2X- = FeX2 log K = 0.44 Al+3 + 3X- = AlX3 log K = 0.36

Carbam+ + X- = CarbamX log K = 0.0

new species added

Pharmaceutically Active CompoundsChemicalFormula Structure Molar

Weight

Primidone C12H14N2O2 218.25

Carbamazepine(CBZ) C15H12N2O 236.26

Sulfamethoxazole(SMX) C10H11N3O3S 253.28

0

1

2

3

4

5

6

7

8

9

10

11

10 11 12 13 14 15 16 17 18 19time [days]

Car

bam

azep

ine

[µg/

L] experiment

model without IX model with IX

retardation

Carbamazepine

degradation due to ozonation

Retardation of CBZC

ol 4

PhACsBromide [mg/L]

CBZ [mg/L]

SMX [mg/L]

Primidone [mg/L]

Bromide [mg/L]

CBZ [mg/L]

SMX [mg/L]

Primidone [mg/L]

time [days] time [days]

Col 4 Col 5

Mod

el R

esul

ts

Mineral Phases3

Mineral Phases

gypsum CaSO4H2O

ferrihydrite Fe(OH)3

aluminum hydroxide Al(OH)3

amorphous SiO2 SiO2

calcite CaCO3

as equilibrium phases (based on log K values)

as kinetic reaction

Lake Tegel water: SI > 0

Calcite Kinetics

Column 3

0SIformm101rrate

0

SIdiss

s/mM103r 8diss

Ca [mg/L] DIC [mg/L]

mM5m0 ?

Redox Reactions4

-5,0

-2,5

0,0

2,5

5,0

7,5

10,0

12,5

15,0

0 2 4 6 8 10 12 14 16 18 20

Time in days

pE

-300

-150

0

150

300

450

600

750

900E

h in mV

Col 2Col 3Col 4Col 5

Experimental Fact (Redox Potential)

microbial activity (Col 2 to 4)

no activity (Col 5)

Microbe

e-Do

nor

e-Ac

cept

or

Ox Ox + e-

Red + e- Red

e- Energy (ATP)

+

Electron Transfer

oxidation (loss of e-)

reduction (gain of e-)

0 10 20-20 -10

0 10 20-20 -10

O2 reduction

denitrification

MnO2 → Mn+2

Fe(3) oxide → Fe+2

SO4-2 reduction

CH4 fermentation

reductions

oxidation of Corg

Sulfide → SO4-2

Fe+2 oxidation

nitrification

Mn+2 oxidationoxidations

pe

half-reactions relevant in Col 2, Col 3, and Col 4

Biodegradation of Organic Matter

Degradation of Organic Matter (CH2O)

CH2O + H2O = CO2 + 4H+ + 4e- oxidation: C(0) C(IV)

O2 + 4H+ + 4e- = 2H2O reduction: O(-II) O(0)

NO3- + 2H+ + 2e- = NO2

- + H2O reduction: N(V) N(III)NO2

- + 8H+ + 6e- = NH4+ + H2O reduction: N(III) N(-III)

SO4-2 + 10H+ + 8e- = H2S + 4H2O reduction: S(VI) S(-II)

electron donors

electron acceptors

less important

Sequential Reduction of Nitrate

no measured data

NO3- NO2

- NOx N2 PON DON NH4+

GW / columns: closed systembioreactor: open system

CH2O Degradation – Enzyme Kinetics

From simple to complex:

1st order kinetics

Michaelis-Menten

Michaelis-Menten e Acceptors

Michaelis-Menten e Acceptors Population Dynamics

d[P]/dt

[S] KS

vmax

½ vmax

PEESSE k

k

k

]S[K]S[v

dt]P[d

S

max

]E[kv 0max

]ES[]E[]E[ 0

kkkKS

steady state

+

+

E

S

ES E

P

Michaelis-Menten Kinetics

Two Principal Approaches

Enzyme Kinetics Population DynamicsMichaelis, Menten 1913 Monod 1942

dynamical variable: substrate [S] biomass or cell density B

deduced from well-defined assumptions about the catalytic mechanism empirical equation

]S[K]S[v

dt]S[d

Smax

]E[kv Tmax

]S[K]S[

Smax

B)(dtdB

]S[K]S[v

dt]S[d

S

max

Mixed-Order Kinetics

]S[Kv

dt]S[d

S

max

maxvdt

]S[d

1st order

zero order

S << KS

S >> KS

S0 = 80 mMKS = 8 mMμeff = 410-7 M/s

Michaelis-Menten

used model parameters

Combination of both Approaches

Number of Enzymes and Biomass amount are correlated.

BY

Ek maxT

)t(BY

)t(dt

]S[d

)t(B)t(dtdB

]S[K]S[)t(

Smax

Yield

ODE System

Applied Model (with e Acceptors)

electron flow

max2S

20

2

B)t(B

]OCH[K]OCH[

dt]OCH[d

maxmax

eff BY

)t(f)t( accepteff0

)t(NOa)t(NOa)t(Oaf)t(f 2231200accept

A non-linear system.

Ther

mod

ynam

ics

Enzyme Kinetics

+

E

H

EH

+

E

2S ESS

+

E

S ESG

+

G

]S[IK]S[v

1S

max

11 K

]H[1I

2S

max

I]S[K]S[v

22 K

]S[1I

3S

max

I1

]S[K]S[v

33 K

]G[1I

competitive inhibition

self-inhibition(HALDANE)

non-competitive inhibition

What About Inhibition ?

B

t Lag Exp Stationary Death

Individual Cell(Cell Growth)

Populations of Cells

Increase in Cellular Mass

and Size

Increase in Total Number of Cells

Difficult to Measure

Bacterial Growth

strain a

enzyme A

enzyme B

strain b

strain cXA ≠ Xa

Population vs. Enzyme Density

pH & pe

time [days]

time [days] time [days]

Col 2 Col 2

Col 3 Col 3

Col 4 Col 4

Col 5

Col 5

pH pe

pH pe

pH pe

pH

pe

Redo

x Dy

nam

ics

time [days]

Redox Dynamic

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,14

0,16

0 1 2 3 4 5 6 7 8time [days]

Oxygen [mM]

Nitrate [mM]

Nitrite [mM]

Ammonium [mM]

Col 3mass

balance

time [days]

Redox Zonation inside Col 3

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,14

0,16

0,18

0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35

Nitrate [mM]

Nitrite [mM]

Ammonium [mM]

Oxygen [mM]initial state

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,14

0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35

after 20 hours

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35

after 45 hours

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,00 0,05 0,10 0,15 0,20 0,25 0,30 0,35

distance inside the column [m]

Nitrate [mM]

Nitrite [mM]

Ammonium [mM]

Oxygen [mM]

after 70 hours

Conclusions5

Conclusion 1

“simple” sand-filled columns

surprisingly complex redox dynamics

Most things are unseen in raw data.

Conclusion 2

Intro

model

A sound combination of

experimentQC

(always) reveal details about a system that you didn’t think of beforehand.

data

Conclusion 3

IntroOnce a model is calibrated by real datascenarios can be simulated:

variation of flow velocity

natural sediments w/o µbiology and/or technical sandlarger columns (upscaling)

column systems (and reactors)