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Hydrochemistry based on REE Data - Some Thoughts & Examples
1. Thermodynamics Workshop 18-19 Jan 2016
School of Earth and Environment, University of Leeds
Hydrochemistry
based on REE Data
Harald Kalka UIT GmbH Dresden
3. Two Principal Approaches
LMA
Law of Mass Action
GEM
Gibbs Energy
Minimization
log K
+ mass balance
G → min
+ mass balance
PhreeqC EQ3/6
Minteq
ChemSage Fact
GEMS-PSI
4. 4
LMA – Law of Mass Action
aA + bB = cC + dD log K
b
eq
a
eq
d
eq
c
eq
}B{}A{
}D{}C{
K
RT303.2
G
Klog
0
ion activity
5. 5
Two Types of Problems
non-ideal solutions (I > 0)
complete & consistent
thermodynamic datasets (log K’s)
the Achilles’ heel of
any hydrochem modeling
6. 6
Non-Ideal Solutions
Activities are introduced
in order to preserve the ideal gas equations
in a non-ideal world
of real solutions.
pV = nRT
{i} = γ ∙ [i]
7. 7
LMA – Main Idea
mole balance
N
1j
i
i,j
}j{K}i{
SN
1i
i,jTOT ]i[]j[
mass action
N master species
NS species
concentration
activity
Note the Asymmetry!
8. LMA – Numerical Solver
8
0}k{K]j[)c,..,c,c(f
S
i,k
N
1i
N
1k
i
i
i,j
TOTN21j
f(x) = 0
Newton-Raphson
N
1
1
c
c
c
x
15. - 15 -
Atomic and Effective Ionic Radii
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
atom
RE+2
RE+3
RE+4
Sc Y La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu
Radius in nm
[Ullmann 2012]
Eu
Yb
atom
REE+3
16. 16
REE Speciation
pH 7.0
pe 4
Y 1e-5
La 1e-5
Ce 1e-5
Pr 1e-5
Nd 1e-5
Sm 1e-5
Eu 1e-5
Gd 1e-5
Tb 1e-5
Dy 1e-5
Ho 1e-5
Er 1e-5
Tm 1e-5
Yb 1e-5
Lu 1e-5
C(4) 3e-5
Cl 6e-5
P 6e-5
F 6e-5
S(6) 6e-5
synthetic input solution
cations: 15 REEs
(total 0.45 meq/L)
anions
(total 0.45 meq/L)
25. 1D Reactive Transport (TRN)inflow=F(t)
Layer A Layer B Layer C
Advection & Dispersion & Reactions
unlimitedNumber of aqueous species
unlimitedNumber of reactive minerals
unlimitedNumber of secondary minerals
unlimitedNumber of ion-exchange species
arbitraryType(s) of Kinetics
PhreeqC-based
(C++)
28. 28
Lessons Learnt (Part I)
Almost all models/software are of high-quality
(and provide the same results).
The main problem:
incomplete/wrong data & lack of experience.
input data, params
thdyn. dataset
range of
applicability
29. 29
Lessons Learnt (Part II)
How to convert measured data (from lab) into
input dataset ?
How to handle uncertainties ?
Still Open Questions in Hydrochemistry:
30. 30
Lessons Learnt (Part III)
Model developers are drawn
to complexity like moth to a flame.
Resist the temptation:
Start with simple & robust models.
avoid 2nd order
corrections etc.
Don’t be too clever.
31. 31
Benefits of Modeling
No model, no data is perfect. But knowing the limits
we are able to
gain deep insight about the system (step by step)
uncover “hidden” domains (inside columns, heaps)
design & interpret lab test
simulate & optimize processes
Need: Healthy mix of practice (lab & field work)
and theory.