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Computer-Aided Construction of Chemical Kinetic Models

Computer-Aided Construction of Chemical Kinetic Models
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Dr.BenjaminClark,United States,Teacher
Published Date:21-07-2017
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Computer-Aided Construction of Chemical Kinetic Models: Towards Predictive Chemistry William H. Green MIT Dept. of Chemical Engineering DOE Combustion Meeting Potomac, MD, May 2014 1 Why do we want predictive fuel chemistry models? • Historically, Fuels have changed pretty slowly… Engines evolved faster, using more-or-less static fuels. Fuel was a constant, not a variable… • But now, fuels are changing – To reduce greenhouse gas, laws require renewable fuels – Conventional light sweet petroleum is getting more & more expensive – Possible to make many future fuels with synthetic biology & chemistry • If fuel is a variable, we need to be able to predict how engine+fuel system depends on fuel composition, not just on engine & operating conditions. – Much faster to explore new concepts on computer than by building prototype engines and synthesizing hundreds of barrels of a new fuel for engine tests • Scientifically, we don’t really understand something until we can make quantitative predictions…and discrepancies between those predictions and experiments reveal opportunities to learn something new 2 Challenge: Lots of alternative fuels to evaluate And many types of engines, too Not practical to synthesize & test every possible future fuel in every possible future engine… …rapid, easy predictions are valuable and needed 3 Automated Predictive Chemical Kinetics: The Concept & Motivation • Rapid, Easy Predictions are valuable & needed – Assess alternative fuels & feedstocks – Design new processes and engines • Hydrocarbon/Fuel chemistry is complicated – many reactions, species: need to automate 4 Fuel Chemistry models are very big • Many chemical intermediates between fuel and CO . 2 • Multiple competing pathways • Nonlinear, sometimes non-monotonic kinetics • Real fuels (gasoline, jet, diesel) have many chemical species – So even initial conditions are complicated • Models built by hand: 1,000 species, 5,000 reactions – Human tries to include just the more important reactions. • Models built by computer: 500 species, 10,000 reactions – RMG software typically considers 30,000 species and 100,000 reactions • most of them are found to be numerically negligible 5 We need this black box reactor/engine design emissions in Computer detail chemistry builds and solves the knowledge engine fuel combustion (clearly documented) performance simulation fuel composition error bars operating conditions Similar issues for fuel-formation chemistry e.g. in refinery 6 Commercial software can solve detailed kinetic simulations… …if one can supply the fuel’s reaction mechanism. Simulation predictions Diff. Eq. solver Very long Simulation list of Interpreter reactions equations (CHEMKIN, with rate Cantera, KIVA, dY/dt = … parameters GTPower) 7 How we construct fuel chemistry models Chemistry knowledge Simulation predictions Unambiguous documentation of assumptions about how molecules react Diff. Eq. solver Very long list of Simulation Interpreter reactions equations (CHEMKIN, with rate Cantera, KIVA, dY/dt = … parameters GTPower) 8 Chemical Kinetic Modeling Challenges • Identify all important reactions & species – But not unimportant species & reactions: how to distinguish? • Estimate all reaction rate coefficients (and properties, e.g. thermochemistry) to sufficient accuracy. – We use Functional Group extrapolations & Quantum Chemistry • Large models pose numerical and computer problems – Very challenging for humans to handle, interpret, debug… …SO WE TRY TO AUTOMATE EVERYTHING We build on prior efforts by combustion research community, e.g. Comprehensive Chemical Kinetics 35 (1997) Advances in Chemical Engineering 32 (2007) 9 RMG algorithm: Faster pathways explored further, growing the model Before: “Current Model” inside. RMG decides whether or not to add species to this model. Final model typically After: 500 species, 8000 rxns Open-Source RMG software. Download from 10 Kinetic Model Predictions Rely on Quantum Chemistry for Thermo, Rates • Functional Group approximation – Compute a few examples of each reaction type with quantum, then use same barrier, A factor for analogous reactions. • Most of our calculations at CBS-QB3 level – Geometries, Vibrational Frequencies from DFT – Single point energies at stationary points at higher level – Extrapolation to Basis Set Limit • Recent calculations use F12 methods – Explicit dependence on distance between every pair of electrons – Much faster basis set convergence • Most calculations rely on common approximations – Rigid-Rotor Harmonic-Oscillator approximation – Conventional Transition State Theory (dividing surface at saddle point) – Simple corrections for internal rotors and tunneling – Modified Strong Collision approx. for k(T,P) Are computed thermo, rates accurate enough?? 11 flow reactors Testing Accuracy of Model Predictions vs. Experiment: CEFRC data on Butanols Pyrolysis (shock tube) Flame RCM Speeds MBMS Shock With collaborations from tube other institutes like Flame Univ. Ghent, NIST. Speed s Rare Situation where detailed data available at many different conditions 12 We used RMG to build a mechanism for butanol pyrolysis and combustion. Four isomers, very different octane numbers. n-butanol sec-butanol iso-butanol tert-butanol Octane number = 86 Octane number = 98 Octane number = 100 More reactive Less reactive RMG considered about 30,000 possible species, selected as important: • 372 chemical species • 8,723 reactions Important k’s with quantum chemistry Shamel S. Merchant, E.F. Zanoelo, R.L. Speth, M.R. Harper, K.M. Van Geem and William H. Green, Combustion & Flame (2013) 13 Model predicted butenes yield from butanols pyrolysis accurately 15 1-Butanol (1-Butene) iso-Butanol (iso-Butene) 2-Butanol (1-Butene) 2-Butanol (2-Butene) Pyrolysis, tert-Butanol (iso-Butene) 10 T 1000 K P 2 bar t seconds 5 Experiments: Van Geem et al. Univ. Ghent 0 0 5 10 15 Experimental butene yield = wt% 14 Predicted butene yield = wt%The kinetic model also quantitatively predicts formation of aromatics from butanols (via rather complicated reaction sequences) Data from K. Van Geem, Ghent pyrolysis of iso-butanol 1000 K, 2 atm, 2 seconds 15 When you change reaction conditions, need to add reactions, compute rate coefficients Predictions of 1000 K pyrolysis model for 1467 K OH concentration H O concentration 2 16 Microsecond H O formation at 1400 K 2 sensitive to different reactions than long-time product formation at 1000 K. Bond scissions dehydration Stanford pyrolysis of n-butanol -9 Early Times 10 sec t=3 ms So… computed improved estimates of bond scissions 17 & dehydrations based on quantum chemistry…. Quantum calcs for k(T,P) sensitive at 1467 K significantly changes predictions OH concentration H O concentration 2 18 Using quantum calcs (rather than rough estimates by analogy) for most sensitive high T reaction k(T,P) improves predictions of shock tube pyrolysis: It is always a good idea to do the quantum calcs for the Sensitive numbers in the model OH concentration H O concentration 2 Experimental data: Stranic et al., Combust. Flame (2012). See also Rosado-Reyes et al., J. Phys. Chem. A (2013). 19 Advanced Light Source allows quantification of dozens of species including key radicals in flames • Flames are analyzed with molecular beam time-of-flight mass spectrometry • Photoionization with tunable synchrotron-generated VUV photons allows identification of species • by mass • by ionization energy Data measured by Nils Hansen (Sandia) at Advanced Light Source (LBNL). Hansen et al. PCCP (2012) Hansen et al. Combust. Flame (2013) 20