1. Modeling Light Gas and Tar Yields from Pyrolysis of Green River Oil Shale Demineralized Kerogen Using the CPD Model Ronald J. Pugmire Departments of Chemical Engineering and Chemistry University of Utah Thomas H. Fletcher and Daniel Barfuss Department of Chemical Engineering Brigham Young University

2. Critical Issues Understand the resource Chemical Structure Mechanistic models that describe the actual chemical processes over a broad rang of heating and pressure conditions Access to the proper analytical tools Proper solid state and liquid NMR data X-Ray photoelectron Spectroscopy FTIR spectroscopy coupled with mass spectroscopy and gas chromatography

3. Previous Work TGA pyrolysis of 3 Green River shale samples – GR-1, GR-2, GR-3 Kerogen extracted from shale using HCl and HF procedure – GR-1.9, GR-2.9, GR-3.9 Kerogen pyrolyzed without shale – 10 K/min – Detailed analysis of gas, tar, and char samples as a function of residence time FTIR GC/MS Liquid-state and solid-state 13 C NMR 3

4. Representative Hydrocarbon Molecule Aromatic clusters Bridges Side Chains Bridges break during heating Lattice statistics tell fraction of detached clusters Vapor pressure determines if detached cluster will evaporate

5. Objectives Model kerogen pyrolysis based on chemical structure Analyze the solid-state 13 C NMR data to compare with pyrolysis model 5

6. Originally developed for coal pyrolysis Based on chemical structure of coal – Lattice of aromatic clusters connected by aliphatic bridges – Thermal decomposition of aliphatic bridges chops up the lattice – Percolation statistics relate fraction of broken bridges to the amount of separated fragments – Vapor pressure used to determine if fragments evaporate – Large fragments crosslink back into the char CPD model (Chemical Percolation Devolatilization)

7. The Chemical Percolation Devolatilization (CPD) Model Includes: NMR for coal structure Chemical mechanism for bridge scission Percolation lattice statistics Vapor pressure model Crosslinking Predicts tar and light gas yields as a function of: Coal type Heating rate Temperature Pressure

8. Bridge Scission Mechanism ££*£* 22 2g12g1 c + 2g 2 kbkb kk kgkg kckc

9. How Does Bridge-Breaking Relate to Mass Release? ðLattice structure (also called network)

10. Types of Lattices A. Coordination number = 3 B. Coordination number = 4

11. Vapor-Liquid Equilibrium and Crosslinking

12. Input Parameters Required by the CPD Model (values given for GR2.9 Kerogen) Number of attachments per cluster (  +1) (i.e., coordination number) ------- 4.5 Fraction of attachments that are bridges (p 0 ) (bridges/bridges+side chains) ------ 0.5 (not measured for kerogen) Molecular weight per aromatic cluster (M cl ) ------ 775 Molecular weight per side chain (M  ) ------ 148 Fraction of bridges that are initially stable (c 0 ) ------ 0.0 Measured by NMR

13. Other Parameters (not usually adjusted) Rate coefficients – One set for coals, one set for biomass, one set for kerogen – Set based on extensive comparisons with data – Uses sequential (not parallel) distributed activation energy – A b, E b,  b, A g, E g,  g, A cr, E cr,  (ratio of 2 A’s) Vapor pressure coefficients – Assumed to be MW-dependent

14. CPD Model Applied to Kerogen Initial CPD Model Tar Calculations Initial Idea Use chemical structure data as inputs to pyrolysis model Use kinetic coefficients from TGA for all rates Initial Results Rates not correct Tar yield way too low

15. Why Try the CPD Model Inputs based on chemical structure Applicable to different hydrocarbon without changing rate coefficients? Predicts tar, char, and light gas Tar MW predicted Predicts effects of pressure on tar distribution Publicly available 15

16. Vapor-Liquid Equilibrium and Crosslinking

17. CPD Model Applied to Kerogen Initial CPD Model Tar Calculations Initial Idea Use chemical structure data as inputs to pyrolysis model Use kinetic coefficients from TGA for all rates Initial Results Rates not correct Tar yield way too low

18. Why Is the Tar Prediction Low? Structure ParameterCoalKerogen Carbon aromaticity54 to 86%20% Arom C/Cluster9 to 208 to 12 Attachments/Cluster4.0 to 5.34.4 to 6.0 Side Chain MW9 to 52131 to 148 Cluster MW237 to 616775 to 946 18 Compare chemical structure parameters: Major differences: Kerogen has lower aromaticity Kerogen has long aliphatic bridges and side chains Vaporized side chains from kerogen are large enough to condense (constitute a major portion of tar)

19. Improved CPD Model Assume that 80% of vaporized side chains are able to condense – Value chosen to fit data Use 1 st order rate from TGA pyrolysis of oil shale – Bridge breaking – Side chain release 19 Nice agreement, but could be improved  Does not match early light gas data Similar agreement for GR1.9 and GR3.9

20. Input Parameters Required by the CPD Model (values given for GR2.9 Kerogen) Number of attachments per cluster (  +1) (i.e., coordination number) ------- 4.5 Fraction of attachments that are bridges (p 0 ) (bridges/bridges+side chains) ------ 0.5 (not measured for kerogen) Molecular weight per aromatic cluster (M cl ) ------ 775 Molecular weight per side chain (M  ) ------ 148 Fraction of bridges that are initially stable (c 0 ) ------ 0.0 Measured by NMR

21. Best-fit CPD Model Curve-fit values of – Bridge-breaking rate – Side chain release rate – Ratio of cleavage to crosslinking – These are bridge variables, not mass variables Assumed MW light gas = 20 amu MW heavy gas estimated from – MW light gas, – MW side chain, and – 80/20 ratio of heavy gas to light gas 21 Very good agreement for GR1.9 and GR3.9 Side chain release (E g ) lower than bridge breaking (E b )

22. Changed Bridge Scission Mechanism ££*£* 22 2g heavy c + 2g light kbkb kk kgkg kckc

23. Best-fit CPD Model Curve-fit values of – Bridge-breaking rate – Side chain release rate – Ratio of cleavage to crosslinking – These are bridge variables, not mass variables Assumed MW light gas = 20 amu MW heavy gas estimated from – MW light gas, – MW side chain, and – 80/20 ratio of heavy gas to light gas 23 Very good agreement for GR1.9 and GR3.9 Side chain release (E g ) lower than bridge breaking (E b )

24. Can we explain the increase in char aromaticity? Aromaticity in char increases from 0.2 to 0.8 Is this increase in aromaticity solely due to preferential release of aliphatic material? 24

25. Why Is the Tar Prediction Low? Structure ParameterCoalKerogen Carbon aromaticity54 to 86%20% Arom C/Cluster9 to 208 to 12 Attachments/Cluster4.0 to 5.34.4 to 6.0 Side Chain MW9 to 52131 to 148 Cluster MW237 to 616775 to 946 25 Compare chemical structure parameters: Major differences: Kerogen has lower aromaticity Kerogen has long aliphatic bridges and side chains Vaporized side chains from kerogen are large enough to condense (constitute a major portion of tar)

26. Best-fit CPD Model Curve-fit values of – Bridge-breaking rate – Side chain release rate – Ratio of cleavage to crosslinking – These are bridge variables, not mass variables Assumed MW light gas = 20 amu MW heavy gas estimated from – MW light gas, – MW side chain, and – 80/20 ratio of heavy gas to light gas 26 Very good agreement for GR1.9 and GR3.9 Side chain release (E g ) lower than bridge breaking (E b )

27. CPD Model Applied to Kerogen Initial CPD Model Tar Calculations Initial Idea Use chemical structure data as inputs to pyrolysis model Use kinetic coefficients from TGA for all rates Initial Results Rates not correct Tar yield way too low