Molecular Modeling and Simulation in Process Engineering
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Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
ASIM Workshop on Fundamentals of Modeling and Simulation
Molecular Modeling and Simulation
in Process Engineering
Hans Hasse1, Jadran Vrabec2
1Lehrstuhl für Thermodynamik, TU Kaiserslautern
2Lehrstuhl für Thermodynamik und Energietechnik, Universität Paderborn
Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Technology Vision 2020: The U.S. Chemical Industry
Fields of Major New Developments
Chemical Supply Chain Information Manufacturing
& Engineering Management Systems & Operations
Science
Engineering Computational Technologies
Computational Computational Process Operations
Molecular Fluid Modeling & Simulation &
Science Dynamics Simulation Optimization
=> Link between Engineering and Chemistry
=> New processes, products, materials
Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Molecular Modeling and Simulation Methods
Time / s
100
Continuum Methods
(ms) 10-3 Mesoscale
Methods
(μs) 10-6
Molecular
Force Fields
(ns) 10-9
Semiempirical
QM
(ps) 10-12 Ab initio
QM
(fs) 10-15
10-10 10-9 10-8 10-7 10-6 10-5 10-4
(nm) (μm) Length / m
Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Moore‘s Law
GFLOPS / GIPS
Year
Further progress from
new simulation methods and software
Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Bridging Scales
Examples
Continuum
Systems
Hybrid
Mesoscale CFD
Systems
MD Large COSMO
Molecular Systems RS
Systems
Born-Oppen-
Quantum heimer MD
Systems
Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Molecular Methods in Chemical Process Industries
Current Applications @ Evonik Degussa:
Catalysis
Thermo-physical data
Polymers
Crystallization
Particle technology
Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
COSMO-RS @ Evonik
Quantum chemistry based method for prediction of
thermo-physical properties
Quantum chemistry based prediction
of energy of molecular interactions
Crude classical assumptions for entropy
Close co-operation between engineers
and quantum chemists
Benchmark against phenomenolgical
group contribution methods (UNIFAC)
Quantitative predictions
Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Force-Field Methods @ Evonik
Example: MD simulations of Water-Polymer interface
Qualitative results
Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Mesoscale Methods @ Evonik
Example: Simulation of particle morphologies
α = 16
α=1
α=4
Semiquantitative results
Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Industrial Applications of Molecular Methods
in Process Engineering
Molecular methods are:
already used
especially attractive if no sufficiently accurate
- experiments
- calculations with phenomenological methods
are possible
recognized as a future key technologyLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Molecular Modelling (Force Fields)
Geometry:
¾ Bond lengths and angles
Electrostatics:
¾ Position and strength
of dipoles, quadrupoles,
partial charges
Dispersion and Repulsion:
¾ Parameters of
Lennard-Jones potentials
Many parametersLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Model Parameters from Quantum Chemistry
Geometry
¾ HF with small basis set (z.B. 6-31G) or DFT methods
Electrostatics from electronic density distribution
¾ MP2 with small polarizable basis set (e.g., 6-311+G**)
¾ Molecule embedded in dielectric cavity for modeling dense fluid
phase (COSMO)
Dispersion and Repulsion
¾ Requires simulation of arrangements of at least two molecules
¾ CCSD(T) or MP2 with large basis sets (TZV or QZV)
¾ Very high computational effort
¾ Unsatisfactory accuracy
Fit to thermo-physical data preferredLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Molecular Simulation Basic Methods
Molecular Dynamics (MD)
Numerical solution of
Newtonian equations of motion
Deterministic
Static and dynamic properties
Monte-Carlo (MC)
Statistical Method
Energetic acceptance criteria
Static properties onlyLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Direct Simulation of Phase Equilibria
Example: Vapor-liquid equilibirum of Ethylene Oxide @ 375 K
3500 moleculesLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Phase Equilibrium from Grand Equilibrium Method
Specs: T, x
Liquid Vapor
Simulation: Pseudo grand canonical simulation
(Specification of μ i ( p ) V, T)
l
¾Chemical potentials
¾Partial molar volumes
μ li ( p ) ≈ μ li ( p 0 ) + v li ⋅ ( p − p 0 ) Result: p, yLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
High Performance Parallel Computing
Scaling on selected hardware platforms:
40
Strider
Execution time / 1000 s
30
20
Cacau
10
XC6000 Nu
1
2
4 8
mb 16 Mo
er o 32 Str za
f pr 64 XC Ca ide rt
oce SX 60 ca r
sso -8 00 u
rs
Own parallel FORTRAN codes:
SX 8 ms2 thermo-physical properties
ls1 nano-scale processesLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Polar Two Center Lennard-Jones Models
μ /Q Vapor pressure
ε typ. deviation
σ < 2%
L
4 Model parameters
ε energy dispersion
σ size
repulsion
L elongation
μ dipole Simulation Exp. (corr.)
or polarity
Q quadrupole Models of 80 simple pure components
Parametrization: VLE data onlyLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Pure Component Models: Extrapolations
Joule-Thomson Inversion
Ethylene
p / MPa
Oxygen
Symbols: Simulation
Linies: Reference EOS
Nitrogen
red: critical data T/KLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Ethylene Oxide
Worldwide annual production
about 18 Mio. tons
Use: PET and anti-freeze
Properties:
- explosive
- toxic
- highly flammable
- cancerogenic
- mutagenic
Explosion @ Sterigenics Intl.,
Ontario, CND (2004):
4 wounded, hall destroyedLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Industrial Property Simulation Challenge 2007
Industrial Fluid Properties
Simulation CollectiveLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Molecular Model of Ethylene Oxide
3 LJ Sites (one for the oxygen atom, one for each methylene group)
1 static point dipole along symmetry axis
Rigid, non-polarizable
Adjustment of five parameters (σO, εO, σCH2, εCH2, μ) to experimental
VLE dataLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
IFPSC Challenge 2007 : Problem Description
Development of a new molecular model for Ethylene Oxide
Prediction of 17 properties in 3 categories
Vapor liquid equilibria / Second derivatives/
thermal properties surface tension
Saturated densities Heat capacity
Vapor pressure Isothermal compressibility
Enthalpy of vaporization Surface tension
Critical properties
Transport properties
Normal boiling temperature
Shear viscosity
Second virial coefficient
Thermal conductivity
Benchmarked to “reference data”Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Deviations from Reference Data
sat. liquid density
sat. vapor density
2nd virial coefficient
uncertainty
vapor pressure of reference
enthalpy of vaporization
normal boiling temperature Round-Robin
critical density model
critical temperature
sat. liquid isob. heat capacity new model
sat. vapor isob. heat capacity score:
sat. liq. isoth. compressib. 331/350
sat. vap. isoth. compressib.
surface tension
sat. liquid shear viscosity
sat. vapor shear viscosity
sat. liq. thermal conductivity
sat. vap. thermal conductivity
-40 -20 0 20 40 60
deviation from
deviation fromexperiment
reference / /%
%Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
IFPSC Party 2007Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Ethanol
δρ = 0,3 % δp = 3,7 %
3 LJ sites plus 3 point charges
Point charges model both electrostatics and H-bondingLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
H-Bonded-Species in Methanol + CO2
Geometrical H-bonding criterion of Haughney et al.
Equimolar mixture @ 350 K, 0.1 MPa
Legend:
Donor:
light blue
Acceptor:
single: orange
double: redLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
1H-NMR Spectroscopy of H-Bonding Mixtures
293,15 K
Methanol - CO2
ppm
338,15 K p = 15 MPa
Experiment
SimulationLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Overview Pure Component Models
Non-polar, 1CLJ Dipolar, 1CLJD Quadrupolar, 2CLJQ Polar, Muti-CLJ
Neon (Ne), Argon (Ar) R32 (CH2F2) Flour (F2) iso-Butan (C4H10)
Krypton (Kr), Xenon (Xe) R30 (CH2Cl2) Chlor (Cl2) Cyclohexan (C6H12)
Methan (CH4) R30B2 (CH2Br2) Brom (Br2) Methanol (CH3OH)
CH2I2 Iod (I2) Ethanol (C2H5OH)
Dipolar, 2CLJD Dipolar, 2CLJD (contd.) Stickstoff (N2) Formaldehyd (CH2=O)
Sauerstoff (O2) Dimethylether (CH3-O-CH3)
Kohlenmonoxid (CO) R20B3 (CHBr3) Kohlendioxid (CO2) Aceton (C3H6O)
R11 (CFCl3) R21 (CHFCl2) Kohlendisulfid (CS2) Ammoniak (NH3)
R12 (CF2Cl2) R12B2 (CBr2F2) R12B1 Ethan (C2H6) Methylamin (NH2-CH3)
R13 (CF3Cl) (CBrClF2) Ethylen (C2H4) Dimethylamin (CH3-NH-CH3)
R13B1 (CBrF3) R10B1 (CBrCl3) Ethin (C2H2) R227ea (CF3-CHF-CF3)
R22 (CHF2Cl) R161 (CH2F-CH3) R116 (C2F6) Schwefeldioxid (SO2)
R23 (CHF3) R150a (CHCl2-CH3) (1,1,2) C2F4 Ethylenoxid (C2H4O)
R41 (CH3F) CHCl2-CH2Cl C2Cl4 Dimethylsulfid (CH3-S-CH3)
R123 (CHCl2-CF3) R140a (CCl3-CH3) Propadien (CH2=C=CH2) Blausäure (NCH)
R124 (CHFCl-CF3) R130a (CH2Cl-CCl3) Propin (CH3-C≡CH) Acetonitril (NC2H3)
R125 (CHF2-CF3) C2H5Br (CH2Br-CH3) Propylen (CH3-CH=CH2) Thiophen (SC4H4)
R134a (CH2F-CF3) (1,1) CHBr2-CH3 SF6 Nitromethan (NO2CH3)
R141b (CH3-CFCl2) CH2F-CCl3 R14 (CF4) Phosgen (COCl2)
R142b (CH3-CF2Cl) (2,2,2) CHClBr-CF3 R10 (CCl4) Benzol (C6H6)
R143a (CH3-CF3) R112a (CCl3-CF2Cl) R113 (CFCl2-CF2Cl) Toluol (C7H8)
R152a (CH3-CHF2) CHF=CH2 R114 (CF2Cl-CF2Cl) Chlorbenzol (C6H5Cl)
R40 (CH3Cl) CF2=CH2 R115 (CF3-CF2Cl) Dichlorbenzol (C6H4Cl2)
R40B1 (CH3Br) C2H3Cl (CHCl=CH2) R134 (CHF2-CHF2) Cyclohexanol (C6H11OH)
CH3I CHCl=CF2 CFCl=CF2 (1,2) CH2Br-CH2Br Cyclohexanon (C6H10O)
R30B1 (CH2BrCl) CFBr=CF2 CBrF2-CBrF2
R20 (CHCl3) CHCl=CCl2Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Molecular Modelling of Mixtures
σA, εA
A A Predictions ξ = 1
or
σAB, εAB
Fit to one experimental
data point p(T,x) oder H(T)
B B
σB, εB
Unlike interaction A-B:
Electrostatics fully predictive
Lennard-Jones parameters from combination rules
Modified σ AB = ( σ A + σB ) /2
Lorentz-Berthelot
ε AB = ξ ⋅ ε A εBLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Vapor-Liquid Equilibrium of
Heptafluoropropane + Ethanol
International
Fluid
Properties
Simulation
Challenge
2006
Data basis:
=> VLE @ 283 K
Problem:
=> H-bonds
Peng-Robinson EOS Simulation,ξ =1 + ExperimentLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Henry’s law constant von Oxygen in Ethanol
International
Fluid
Properties
Simulation
Challenge
2004
□, ∆ , Experiment Simulation, ξ = 1 Simulation, ξ fittedLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Extrapolation to multicomponent mixtures
R14 + R23 + R13
+ Experiment
PR-EOS, kij fitted to
binary subsystems
Simulation, ξ fitted to
binary subsystems
Fully predictive
No ternary
parametersLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
MD Simulation of Nanoscale Processes: Condensation
N = 40 000
1 CLJLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
MD Simulation of Nanoscale Processes: Condensation
N = 40 000
1 CLJLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Prediction of Nucleation Rates
Ethane
Simulation
Class. nucleation theory
Laaksonen et al.
Carbon Dioxide
Simulation
Class. nucleation theory
Laaksonen et al.Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Molecular Simulation of Hydrogels
What are Hydrogels?
Three-dimensional hydrophilic polymer networks
Extreme swelling/shrinking
Very sensitive to surroundings & conditions
Examples for applications:
Super-absorber
Contact lenses
Drug Delivery
Sensors
200 µm
Actors (e.g., micro-valves) 3 actors
Biocatalysis
flow channelLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Swelling of Hydrogels
Parameters: Influence of temperature
Temperature
pH-value
Theta-
Salt(s) temperature
Solvent(s)
Co-polymers
Crosslinker
PNiPAM, MBA
PNiPAM by electron-microscopeLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
MD-Simulation of Hydrogels
• Mainly PNiPAM (numerous experimental data)
PVA PNiPAM PAA
• Solvents: Water, Ethanol, aqueous NaCl solution
• Temperatures: 260 K - 340 K
• Force fields from literatureLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
MD-Simulation: Collapse of Hydrogel
primitive PVA-network
39Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Force Field Study
PNiPAM Water: SPCE Water: TIP4P
Gromos96 UA - -
Gromacs53a6 UA - +
OPLS AA ++ +
Legend:
Default Water model of force field
- Temperature dependence not observed
+ Temperature dependence observable
++ Temperature dependence reasonably predictedLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
MD-Simulation PNiPAM-Chains
T < TΘ
T > TΘ
41Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Survey of Coordinated Research Programs
DFG SPP 1155:
Molekulare Modellierung und Simulation in der Verfahrenstechnik
ProcessNet Arbeitsausschuss MMS:
Molekulare Modellierung und Simulation für das Prozess- und
Produktdesign
DFG SFB 716:
Dynamische Simulation von Systemen mit großen Teilchenzahlen
DFG TFB 66:
Molekulare Modellierung und Simulation zur Vorhersage von Stoffdaten
für industrielle Anwendungen
BMBF IMEMO:
Innovative HPC-Methoden und Einsatz für hochskalierbare Molekulare
Simulation
SFB 716Lehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Summary
Molecular Modelling and Simulation in
Process Engineering
used in industry
high potential is recognized
future key technology
truly interdisciplinary field
Co-operation between
¾ Engineering
¾ Natural Science
¾ Computer Science
¾ MathematicsLehrstuhl für Thermodynamik
Prof. Dr.-Ing. H. Hasse
Thanks to co-workers…
Jürgen Stoll
Thorsten Schnabel
Gimmy Fernandez
Bernhard Eckl
Isaiah Huang
Martin Horsch
Thorsten Merker …and colleagues from industry
Gabriela Guevara Johannes Vorholz (Evonik)
Jonathan Walter Robert Franke (Evonik)
Stephan Deublein Bernd Eck (BASF)
Cemal Engin Manfred Heilig (BASF)You can also read
