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Datos y Estimación de la Viscosidad de Líquidos Iónicos
Data and Estimation of Viscosity of ILs
Jéssica Muñoz JeraldoUniversidad de La Serena
International Workshop
Ionic Liquids: Experimental Data and Property Estimation
Viscosity of Ionic Liquids
Viscosity describes a fluid’s internal resistance to flow and may be thought of as a measure of fluid friction.
A low viscosity is generally desired to use IL as a solvent, to minimize pumping costs and increase mass transfer rates.
Higher viscosities may be favorable for other applications such as lubrication or use in membranes.
Viscosity of Traditional Solvents andILs
1033[bmim] [MDEGSO4]
370[bmim] [PF6]
43[emim] [TfO]
38[emim] [BF4]
401,2-propylene glycol
16Ethylene glycol
1.00Agua
0.60Benceno
0.22Diethyl ether
Viscosidad mPa sLíquido
Temperature Effects on Viscosity
Viscosidad versus temperatura para tres líquidos iónicos: [bmim] [PF6] (▲), [bmim] [BF4] (■) y [emim] [TFO] (●).
0
50
100
150
200
250
300
350
400
20 30 40 50 60 70 80 90
Tempetarura (°C)
Visc
osid
ad (c
P)
Predictive Equations
Nombre Parámetros Fórmula Método de Van Velzen, Cardoso y Langenkamp
B, T0 )TB(Tlogη 10
1L
−− −= B is determined from group contribution
Método de Souders
I, M, ρ 2.9ρ
MI)log(log10η −=
I is determined from group contribution
Método de Thomas
ρ, θ, Tr ⎟⎟⎠
⎞⎜⎜⎝
⎛−= 1
T1θ
ρ0.58.569ηlog
r
θ is determined from group contribution
Nombre Parámetros Fórmula Método de Orrick y Erbar
M, ρ, A, B
TBA
ρMηln +=
A and B are determined from group contribution
Rheochor M, ρ
nbl,
0.125b
ch 2ρρ)M(10ηR
+=
The Reochor is determined from group contribution
Método de Przezdziecki y Sridhar (PS)
V, V0, E
( )0
0L VVE
Vη−
=
)/T11.58(T0.0424T0.23P0.10M12.94V1.12E
cffc
c
−+−++−=
0.894)/T0.342(TV2.02T0.0085V
cf
mc0 +
+−= ω
Correlating Equations
Nombre Parámetros Fórmula Ecuación de Andrade
A, B TBAeη = Ecuación de Thorpe y Rodger
α, β, C 2βTαT1
Cη++
=
Ecuación de Vogel
A, B, C
CTBAlnη +
+=
Artifical Neural Netwoks
This is the method we have been exploring
For this we need accurate viscosity data
1-butyl-3-methylimidazoliumtetrafluoroborate [bmim][BF4]
0
50
100
150
200
250
300
275.0 295.0 315.0 335.0 355.0 375.0
Temperatura (K)
Visc
osid
ad (c
P)
[BF4] Ionic Liquids
050
100150200250300350400450
280,0 290,0 300,0 310,0 320,0 330,0 340,0 350,0 360,0
Temperatura (K)
Visc
osid
ad (c
P)
hmim
moimbmim
emim
1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [bmim][bti]
0
20
40
60
80
100
120
270.0 290.0 310.0 330.0 350.0 370.0
Temperatura (K)
Visc
osid
ad (c
P)
1-octyl-3-methylimidazoliumhexafluorophosphate [bmim][C8S]
0100200300400500600700800900
1000
290,0 300,0 310,0 320,0 330,0 340,0 350,0
Temperatura (K)
Vis
cosi
dad
(cP)
Criteria for Data Selection
Same literature source (NIST).
Data follow the expected tendency and behaviour.
Authors explain accuracy of data.
Authors explain purity of samples.
Artificial Neural Network
Numerical method to process information using data for learning the relation between a property(viscosity) and the variables that may depend such a property (temperature, density, structure…)
What Have We Done…
We have explored Artifical Neural Netwoksfor correlating viscosity.
We have included group contributions into theneural network.
viscosity data from the literature
variables that determine viscosity (T, ρ, groups, M?)
Matlab code
ANN model
0023125.1111.3181.244288.2[ESO4][emim]
0023125.1111.3181.249278.2[ESO4][emim]
003359.06139.27451.022343.0[Ac][bmim]
003359.06139.27451.030333.0[Ac][bmim]
003359.06139.27451.037323.0[Ac][bmim]
003359.06139.27451.044313.0[Ac][bmim]
03238421.35242.71.132343.0[doc][N4444]
03238421.35242.71.136333.0[doc][N4444]
03238421.35242.71.139323.0[doc][N4444]
03238421.35242.71.143313.0[doc][N4444]
2051280164.421.394343.0[bti][hpy]
2051280164.421.400333.0[bti][hpy]
2051280164.421.405323.0[bti][hpy]
2051280164.421.410313.0[bti][hpy]
2051280164.421.415303.0[bti][hpy]
2051280164.421.418298.0[bti][hpy]
2051280164.421.421293.0[bti][hpy]
2051280164.421.426283.0[bti][hpy]
>C<>CH--CH2--CH3M(an.)Mcatρ(g/cm3)T (K)anióncatión
2.220[ESO4][emim]
2.489[ESO4][emim]
1.623[Ac][bmim]
1.792[Ac][bmim]
1.987[Ac][bmim]
2.217[Ac][bmim]
2.614[doc][N4444]
2.878[doc][N4444]
3.167[doc][N4444]
3.502[doc][N4444]
1.204[bti][hpy]
1.322[bti][hpy]
1.462[bti][hpy]
1.623[bti][hpy]
1.806[bti][hpy]
1.903[bti][hpy]
2.025[bti][hpy]
2.276[bti][hpy]
log visc. (cP)anióncatión
The optimum architecture was foundby trial and error.
5,10,15,10,10,15,15,15,110,20,20,20,1
ANN Architecture
Several Options For The IndependentVariables Were Explored
ρ, MG grupos
ρ, ni gruposlog η
Vm, ni grupos
ρm, ni grupos
ρ, ni gruposη
MG = ni Mi
Results
7,30,613,91,37,31,018,01,7ρ, MG grupos
7,20,613,91,37,31,018,51,7ρ, ni gruposlog η
99,09,871,99,1122,58,3146,513,6Tc, Vc, Pc, ni grupos
173,414,295,79,870,78,359,16,6Vm, ni
grupos
78,49,161,07,875,78,7156,69,2ρm, ni
grupos
4708,6174,0110,29,3138,411,8110,210,1ρ, ni gruposη
|%Δη|m|%Δη|a|%Δη|m|%Δη|a|%Δη|m|%Δη|a|%Δη|m|%Δη|a
5,10,10,10,15,10,10,15,25,15,10,1
0,0E+00
5,0E+03
1,0E+04
1,5E+04
2,0E+04
2,5E+04
0,0E+00 5,0E+03 1,0E+04 1,5E+04 2,0E+04 2,5E+04
Viscosidad exp. (cP)
Visc
osid
ad c
alc.
(cP)
Conclusions
A consistent hybrid method, neural network plus a group contribution method (GCM+ANN) has been used with success for correlating the viscosity of ionic liquids.
The capabilities of the ANN to predict viscosities has not been explored, although the good correlation guarantees acceptable results.
After we have an appropriate network, the viscosity of other ionic liquids could be predicted.