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)

Type of Approaches

Correlating equations.

Generalized Predictive equations.

Neural Network models.

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.

The groupsused in the literature forcalculatingcriticalpropertieswere used.

Group Definition

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.

Thanks to all of youGracias a todos(as)