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Page 1: THERMOPHYSICAL PROPERTIES OF WORKING FLUIDS

THERMOPHYSICAL PROPERTIES OF WORKING FLUIDS

For Energy Efficiency

Christophe COQUELETProf. Génie des Procédés Mines ParisTech

Directeur CTP

Mines ParisTech, PSL – Research University, CTP - Cen tre Thermodynamique des Procédés, 35 rue St Honor é, 77305 Fontainebleau Cedex, France

Centre of Thermodynamics of Procédé[email protected]

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MOTIVATIONS

� New regulations concerning utilisation of fluids (F-gas)� Several candidates for various applications: refrigeration, heat

pumps, climatisation, ORC, etc..� Numerous fluids (pure component or mixture) can be used� Necessity to use predictive tools in order to select the best

candidates for each applications (Refrigeration, heat pump, ORC, etc..)� Thermodynamic: Predictive equations of state have to be developedconsidering group contribution or corresponding state approach� Transport properties: Correlations or models like SUPERTRAPP canbe used to estimate thermal conductivity, viscosity for example

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PrReA Nu βα= Nu

λhD=Nusselt number:

Reynolds number:

Prandlt number:

Heat transfer coefficient: Rµ

ρvDe=

µCpr =

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MOTIVATIONS� One strategy concerning the selection of working fluids?

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JIP or Industrial Chaire

The specific objectives are as follows:

•To identify the gaps in available literature on the phase behaviour (VLE, bubble pressure,critical point, heat capacity) and fluid properties (thermal conductivity, viscosity anddensity...) for working fluids: creation of a database•To perform phase behaviour measurements for binary systems and for representativemulti‐component systems.•To provide real information concerning the energy efficiency of working fluids(experiments and simulations).•To evaluate the viscosity and density over a wide range of pressure and temperatureconditions.•To develop molecular simulation models (force field) in order to generate thermophysicalproperties of new generation of working fluids (pure component and mixtures)•To develop predictive equation of state with or without molecular based•To develop/tune predictive tools to predict the phase behaviour, transport properties, forworking fluids•Interfacial tension

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JIP or Industrial Chaire

The suggested topics are:• Development of database with compilation of data relevant to the project.• Critical evaluation of existing data• Molecular simulation work

– Development of force field– Monte Carlo and molecular dynamics simulations

• Phase behaviour : VLE, bubble pressure, critical point properties• Volumetric properties for temperature lower or higher than critical temperature• Calorimetric and thermal properties (thermal conductivity)• Viscosity• Interfacial tension• Development of predictive equations of state• Evaluation of the working fluids considering process simulation aspects and test

on real machine• Water content to avoid frost point• Compatibility with lubricant

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MOTIVATIONS� Example of work done for Edf Chatou (Fluid selection) – Franck David

• The selection of the fluids is mainly based on their energetic performances evaluated on energetic machines.

• The performance (COP) is generally evaluated from a process simulator and confirmed after several tests on existing machines.

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Example of ORC (Bo LIU, 2014)Simulated using ThermoptimTM

Collaboration CES- CTP-PERSEE

TS diagram of two stage ORC

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MOTIVATIONS� Predictive equations of state

• Objectives

• Phase diagrams representation

Knowledge of phase diagram is essential (azeotrope, critical point, relative volatility, conditions of apparition of liquid liquidequilibrium, solubility in lubricant

• Density predictions

Estimation of the densities of both vapor and liquid phasse with the maximimum of accuracy

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Scott and van Konynenburg classification.

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MOTIVATIONS� Parameterization of equations of state

• Necessity of utilization of predictive EoS

– Group contribution approach– binary interaction parameter is calculated considering Group contribution

method.– Necessity of experimental works

• Possibility to consider molecular simulation calculation

– Monte Carlo approach for phase diagram and density predictions– Fluid dynamic to have the prediction of transport properties– Validation of potential of interaction by comparison with experimental data

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Molecular Simulation

� Monte Carlo methods → Phase equilibrium properties for pure fluids and mixtures (densities, vapor pressures, Pxydiagrams…)

� Molecular dynamics methods → Transport properties (viscosities, thermal conductivities…)

� Necessity to represent the potential of interaction (force field) between the molecules using sevral methods likeUnited Atom, Trapp, AMBER, etc..

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Lennard Jones type potential of interaction (Wikipedia)

−=60

120

0 r

r

r

rUU

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Molecular simulation

• Example of Monte Carlo simulation on HFO 1234 yf

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Equilibrium

Liquid phase box Vapor phase box

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Molecular Simulation

• Results concerning HFO 1234yf

– Comparison of results with with Refprop 9.0

Development of Force Field concerning the new gener ation of refrigerant (HFO)

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240

260

280

300

320

340

360

0 500 1000 1500

T (

K)

Density (kg m-3)

R-1234yf

REFPROP

0

0.5

1

1.5

2

2.5

3

3.5

220 270 320 370

P (

MP

a)

T (K)

R-1234yf

REFPROP

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Experimental Approach� Thermodynamic properties

• Two main methods:

– Open circuit methodsCirculation of fluid (limiting activity coefficient,

critical point, density

– Close circuit methodsFixed or variable volume

Synthetic and analytic methods

Measurements of T, P, V and compositions for equilibrium properties

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Experimental Approach� Vapor Liquid Equilibrium measurement

• Static analytic method

• Temperature is maintained constant

• Component are added using gas cylinder

• Phase sampling (ROLSI(TM))

• Gas Chromatography for the determination of the composition of each phase

• Determination of experimental uncertainty using NIST standard

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EC

ST

GC

TR

SD

V1

C1LV1

EC

PP

MS

TR

PT

LV2

TP

PT

TR

V2VP

V3

LB

SC

PC

LS VSV4

C2

V6

VP

PP

TP

TR

V5

EC: equilibrium cell; LV: loading valve; PP: platin um resistance thermometer probe; PT: pressure transducer; C1: mor e volatile compound; C2: less volatile compound; GC: gas chrom atograph;LS: liquid sampler; VS: vapor sampler; SC: sample c ontrolling;PC: personal computer; VP: vacuum pump.

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Experimental Approach� Vapor Liquid Equilibrium measurement

• Static analytic method

• Temperature is maintained constant

• Component are added using gas cylinder

• Phase sampling (ROLSI(TM))

• Gas Chromatography for the determination of the composition of each phase

• Determination of experimental uncertainty using NIST standard

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Experimental Approach� Vapor Liquid Equilibrium measurement

• Static analytic method

• Temperature is maintained constant

• Component are added using gas cylinder

• Phase sampling (ROLSI(TM))

• Gas Chromatography for the determination of the composition of each phase

• Determination of experimental uncertainty using NIST standard

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Experimental Approach� Critical point measurement

Critical points were determined by observing the cr itical opalescence (dynamic method):

1) A mixture of known overall composition is prepared and sen t in the cell2) The temperature is increased and the flow rate is regulate d in order to

maintain the meniscus in the middle of the cell3) At the critical point, the cell becomes orange and the meni scus disappears

from the middle of the cell. T C and PC are recorded.16

PSPS

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Experimental Approach� Critical point measurement

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Experimental Approach� Density measurements

• Vibrating tube densimeter

• The measurements are based on the indirect synthetic method. The method is based on the relation between the vibrating period of a dimensional resonator and its vibrating mass.

• The main part of the apparatus is the densimeter cell DMA-512P (Anton PaarKG).

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Flow diagram of the vibrating tube densimeter. (1): loading cell; (2a) and(2b): regulating and shut-off valves; (3): DMA-512P densimeter; (4): heatexchanger; (5): bursting disk; (6): inlet of the temperature regulating fluid; (7a)and (7b): regulating and shut-off valves; (8): pressure transducers; (9): vacuumpump; (10): vent; (11): vibrating cell temperature probe; (12): HP 53131A dataacquisition unit; (13): HP34970A data acquisition unit; (14): bath temperatureprobe; (15): principal liquid bath.

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Examples of Results� VLE measurements

• Vapor Liquid Equilibrium of HFC 245fa (1) +Isopentane (2) at 373.19K• Application for heat pump (used by EDF Les Renardières)• Azeotropic behavior

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0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

P r

ess

ion

/M

Pa

x1,y1

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Examples of results

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0.0 0.5 1.0 0.0

2.0

4.0

6.0

8.0

x1, y1

P/MPaT1 = 283.21 K

T2 = 293.18 K

T3 = 298.11 K

T4 = 308.20 K

T5 = 323.18 K

T6 = 338.24 K

T7 = 353.25 K

300.0 350.0 400.0

2.0

4.0

6.0

8.0

T/K

P/MPa

CO2

R1234ze(E) R1234yf

(a)

0

1

2

3

4

5

6

0.0 0.2 0.4 0.6 0.8 1.0

Re

lati

ve

vo

lati

lity

x CO2

Relative volatility :Juntarachat et al. (2014) (▲) at 308.20 K; Raabe (MS) (2013) (∆) at 310.92 K.

P-T and P-x projections including experimental point and modelling using PPR78

�Interest in climatisation and/or refrigeration (low pressure glide)�The binary system was investigate using two equipments (critical point and static analytic type)�R1234yf is considered as one group�Parameters are fitted considering both VLE and critical point experimental data�Good agreement is observed with molecular simulation calculation

� CO2 + HFO 1234yf

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Equation of state� Coquelet et al. 2016

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P-ρ diagram for R-1234yf. (∆) Experimental data ; (▲) Confidential Experimental data; (──) REFPROP; (×) Critical Point: REFPROP (Tc = 367.85K); (- - - -) NEoS; (…...) PT-EoS; (─ ─ ─) PR-EoS. Out of saturation: Tr = 0.7, 0.9, 1.0, 1.1.

P=RT/(v-b)- a(T)/(v2+ubv+wb2 )

u= 1+ c/b

w= -u

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Equation of state� Coquelet et al. 2016

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P-ρ diagram for R-1234yf. (∆) Experimental data ; (▲) Confidential Experimental data; (──) REFPROP; (×) Critical Point: REFPROP (Tc = 367.85K); (- - - -) NEoS; (…...) PT-EoS; (─ ─ ─) PR-EoS. Out of saturation: Tr = 0.7, 0.9, 1.0, 1.1.

P=RT/(v-b)- a(T)/(v2+ubv+wb2 )

u= 1+ c/b

w= -u

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Derivatives Properties

• Ideal gas or Liquid

• Real Gas

• Residual Enthalpy

• entropy

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( ) ∫=∗T

T

Pi

ref

idTCTh

( ) ( ) ( ) PThhThxPTh ,,, ∗∗ −+=

( ) dvT

PT

v

Pvhh

v

vTxPT ∫

∂∂+

∂∂=− ,,

( ) ∫∫ −=∗P

P

T

T

Pi P

dPRdT

T

CTs

ref

i

0

( ) dvv

R

T

Pss

v

vPT ∫

∂∂=− ,

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Examples of results� CO2 + HFC 116

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Similar behavior as the CO 2 + ethane system.Critical positive azeotropy accurately predicted

+CO2

0.0 0.5 1.0 0.0

20.0

40.0

x1,y1

P/bar

0.0 0.5 1.020.0

40.0

60.0

x1,y1

P/bar

C2F6T1 = 227.60 K kij = 0.0870T2 = 253.29 K kij = 0.0798T3 = 273.27 K kij = 0.0753

T1 = 283.24 K kij = 0.0734T2 = 291.22 K kij = 0.0720T3 = 294.22 K kij = 0.0715T4 = 296.72 K kij = 0.0711

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Examples of results� HFO 1216

• Comparison between experimental data (vibrating tube densimeter) and modelling• Patel Teja EoS is used• Necessity to apply a correction (Crossover EoS)

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____: Crossover PT (CR-PT), -------: Classical PT, ×: point critique, (∆) données expérimentales de Coquelet et al.

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Density measurements

• Mixture R419A (R125/R134 a/DME with 77/18/4 mol %

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0

20

40

60

80

100

120

140

160

0 200 400 600 800 1000 1200 1400 1600

Masse volumique [kg/m3]

Pre

ssio

n [b

ar]

30°CPoints de roséePoints de bulle70°C50°C5°C-20°C-10°C0°C80°C75°C

-20°C

70°C

30°C

80°C

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CONCLUSION� And perspectives

• Utilisation of predictive models in process simulator

• Evaluation of thermodynamic properties of fluids and their mixtures

• Compatibility with lubricant

• Selection of fluids based also on their transport properties

• Development of predictive equation of state

• Collection of experimental data and measurement of new experimental data (VLE, critical point and density): development of equipment

• Development of specific force Fields and comparison with transport properties data

• Measurement of heat capacity and enthalpy of mixing27