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Experimentation Verses Prediction
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Method For The Estimation Of Thermal Conductivity For Non- - - PowerPoint PPT Presentation
Method For The Estimation Of Thermal Conductivity For Non- - - PowerPoint PPT Presentation
University of KwaZulu Natal Chemical Engineering A New Group Contribution Method For The Estimation Of Thermal Conductivity For Non- Electrolyte Organic Compounds Onellan Govender 205502080 Experimentation Verses Prediction 2 Thermal
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Thermal Conductivity Required for
Equipment design Cost-effective and safe
plant design
Simulation packages Scale up Calculation of transfer
coefficients and dimensionless numbers
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Thermal Conductivity Required for
Equipment design Cost-effective and safe
plant design
Simulation packages Scale up Calculation of transfer
coefficients and dimensionless numbers
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SLIDE 6
Previous Work Done
Normal boiling point (Rarey and Cordes 2002
and Nannoolal et al. 2004)
Critical property data (Nannoolal et al. 2007) Vapour pressures (Nannoolal et al. 2008 and
Moller et al. 2008)
Liquid viscosity (Nannoolal et al. 2009)
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Current Work
Bruce Moller – Gamma infinity in water,
alkanes, alcohols
Eugene Olivier – Surface Tension Onellan Govender –Thermal Conductivity
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SLIDE 8
Thermal Conductivity - λ
Theoretical Contributions and
Considerations
Critical Enhancement
( , ) ( ) ( , ) ( , )
n
- n
c n
T T T T
q
J
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λ (W/m.K)
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Thermal Conductivity of Propane as f(T,P)
( , ) ( ) ( , ) ( , )
n
- n
c n
T T T T
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Critical Enhancement
Selected isotherms for CO2 depicting the critical enhancement phenomenon (Mathias et al. 2002)
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Factor of 16 Tc + 0.25K Tc + 0.33K Tc + 0.74K Tc + 2.09K
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Experiments and Data Correlation Prediction Models
Corresponding States General Correlations Family Methods Group Contribution Method
Correlation and Prediction
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General Correlation Methods
Sato and Reidel (1977) Lakshmi and Prasad (1992)
Family Methods
Latini et al. (1977)
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0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 200 250 300 350 400 450
Thermal Conductivity (W/(m.K)) Temperature (K)
Thermal Conductivity of Octane as f(T) at 1atm
Lakshmi & Prasad Sato & Riedel Experimental Data Linear (Experimental Data)
General Correlation Methods
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0.1 0.11 0.12 0.13 0.14 0.15 250 260 270 280 290 300 310 320
Thermal Conductivity (W/(m.K)) Temperature (K)
Thermal Conductivity of Methylcyclopentane as f(T) at 1atm
Lakshmi & Prasad Sato & Riedel Experimental Data Linear (Experimental Data)
General Correlation Methods
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Previous Group Contribution Methods
Sakiadis and Coates (1955 , 1957) Robbins & Kingrea (1962) Nagvekar & Daubert (1987) Assael, Charitidou & Wakeham (1989) Sastri and Rao (1993) Rodenbush, Viswanath & Hsieh (1999) Sastri and Rao (1999)
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SLIDE 16
Literature Review & Method Test
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Methods Sato & Riedel (1997) Nagvekar & Daubert (1987) Lakshmi & Prasad (1992) Sastri & Rao (1993)
RMD (%) 19.81 16.64 23.41 14.11 Number of Components 500 322 500 469
Component Classes
Hydrocarbons Oxygen Compounds Ethers Aldehydes Aromatic Hydrocarbons Carboxylic Acids Esters Nitrogen Compounds Halogen Compounds Alcohols Ketones Amines
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Data Filtration and Validation
Figure : Data points for n-butane
(1atm isobar; 135.75 < T (K) < 272.65)
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DDB – 100 515 data points for 876 components
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Data Filtration and Validation
Figure : All experimental data points for n-butane
(135.75 < T (K) < 423.61; 101.325 < P (kPa) < 70000)
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SLIDE 19
Model Idea
( , ) ( ) ( ) ( 101.3 )
Ref Ref
T P T f T T g P kPa
REF REF P
T T T
101.325
P REF
P P T T P T
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Reference Value Separate group contributions for the 3 parts Temperature Dependence Pressure Dependence
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Software Development
The DDB (Artist) Microsoft Office SQL & ADO
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Future Work
Select a regression model and determine empirical
parameters for all compounds
Evaluation & definition of structural groups Regression for structural or bond contributions Testing using a test set of thermal conductivity data Optimisation of structural or bond contributions
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SLIDE 22
Summary
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Importance of prediction methods Thermal Conductivity Review of available prediction methods Evaluation of methods Filtration and validation of data from DDB Software Work left to be done
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Acknowledgements
Prof. D. Ramjugernath from UKZN Prof. J. Rarey and Prof. J. Gmehling from Oldenburg
University
Bruce Moller and Eugene Olivier DDBST Software and Separation Technology South African NRF and German DLR (BMBF)
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