Prediction of Tar and Light Gas During Pyrolysis of Black Liquor and Biomass

Transcription

1 Prediction of Tar and Light Gas During Pyrolysis of Black Liquor and Biomass Harland R. Pond Thomas H. Fletcher Larry L. Baxter Chemical Engineering Department Brigham Young University Advanced Combustion and Engineering Research Center (ACERC)

2 Wanted: Pyrolysis Model Model to describe pyrolysis of biomass and black liquor fuels Model based on individual components Cellulose Hemicellulose Lignin Model suitable for inclusion in FLUENT and PCGC 3 CFD packages

3 Chemical Percolation Devolatilization (CPD) Model Approach For coal, CPD looks at a lattice of aromatic rings (Fletcher et al.) Bridges between rings thermally rupture (kinetic scheme) Lattice statistics determine the fraction of free fragments Vapor pressure of free fragments determines tar yield Function of coal type, heating rate, temperature and pressure

4 CPD Lattice Structural Parameters required Number of attachments per cluster (σ + 1) Fraction of attachments that are initially bridges (p o ) Molecular weight per cluster (M CL ) MW per side chain (M δ )

5 CPD Parameters Structural parameters M d,, p o, M CL, σ + 1, elemental analysis Fraction of char links (c o ) Kinetic Parameters Kinetics for two competing reactions

6 Model Goals Each biomass fuel consists of cellulose, lignin and hemi-cellulose (hard- and softwood) Black liquor consists of Kraft lignin and residual carboxylic acids Develop kinetic and structural parameters for each component Use mass-weighted distribution of CPD predictions for individual components

7 CPD Application to Biomass Linear structure of cellulose is modeled with c o and p o Bridge weights for cellulose type components changed to model ether linkages

8 Lignin Chemical Model Lignin is similar to low rank coal with lattice structure Coniferyl, sinapyl and p-coumaryl acids base cluster in the CPD model

9 Setting Parameters Structural parameters (p o, M CL, M δ and σ+1) set with 13 C NMR measurements and known structures Kinetic parameters initially taken from previous research (Sricharoenchaikul, Fletcher, Serio, Azevedo) 3 parameters need to be fitted to volatile yield data (c o, E c, ρ)

10 13 C NMR Analysis on Black Liquor Structural parameters for black liquor composite obtained from 13 C NMR analysis Parent samples sent to Pugmire and Solum at the University of Utah for analysis Reacted samples from flat flame burner experiments were also sent for analysis (Webster, BYU, 2002)

11 O R O * O *? * OH * OCH 3 R OCH 3 CH 3 * OCH 3 OH OCH OCH 2 CH 2 CH H * ss

12 Structural Parameters Parameters obtained from theory for hardwood and softwood lignin and Kraft lignin CNMR parameters used for composite Black Liquor parameters Parameter Lignin Lignin Kraft Lignin Black Liquor Structural Parameters Hardwood Softwood Composite MW Cl M d p o σ

13 Pyrolysis Data Volatile yield data required for fitting the model Need data with heating rates and yields at different temperatures Data obtained Lignin: Nunn, et. al. heated grid data Black Liquor: BYU Furnace data % Yield CPD Prediction of Lignin Volatile Yields 1000K/s 30s hold, data from Nunn, T.R., et. al. Ind. Eng. Chem. Process Des. Dev., 24:844 (1985) Temp /K Gas Yield Tar Yield

14 CPD Model in Previous Research Kinetic parameters for biomass and black liquor identified from original and previous research (Serio) Some structural parameters in previous applications of the CPD model not set correctly misunderstood Sheng & Azevedo (2000) Biomass Sricharoenchaikul (2002) Black Liquor Sricharoenchaikul fit model only to tar yields Measured tar yields were too low (not all tar accounted and carbon balance not closed)

15 Optimization and Data Fitting Optimization required for three parameters (E c, ρ, c o ) CPD model used with optimization program to find global optimized parameter values Values of E c = 0 gave best model results Value of ρ set to 3.9 Model fit to data through fine tuning kinetic parameters and c o

16 CPD Component Parameters Parameter Lignin Lignin Kraft Lignin Structural Parameters Hardwood Softwood MW M δ Kinetic Parameters E b, kcal/mol A b, s E E E+15 σb, kcal/mol E g, kcal/mol A g, s E E E+15 σg, kcal/mol ρ E c, kcal/mol E cross, kcal/mol A cross, s E E E+15 c o p o σ fst

17 Results

18 Lignin Yield % CPD Prediction of Lignin Volatile Yields 1000K/s 30s hold, data from Nunn, T.R., et. al. Ind. Eng. Chem. Process Des. Dev., 24:844 (1985). Secondary tar reactions to form light gas Temp /K Tar Yield Light Gas Yield CPD Tar Prediction CPD Light Gas Prediction Yields fit heated grid data well, secondary tar reactions not modeled

19 Black Liquor Yield Black Liquor Volatile Prediction with CPD 100K/s with 10 min hold data from BYU furnace data Temp /K Total Volatility Data CPD+Model Na 2 CO 3 + 2C 2Na + 3CO Na 2 CO 3 + C 2Na + CO + CO 2 Yields fit well with furnace data, sodium carbon reactions not modeled

20 Future Work More volatile data needed for black liquor, cellulose and hemi-cellulose Integration of a program calculating component composition from elemental analysis Predict volatile yield from weighted average of biomass components

21 Summary & Conclusion CPD modified slightly to describe biomass Previous attempts to use CPD model were incomplete Developed chemical structural and kinetic parameters for Kraft lignin and lignin based on theory, literature review and curve-fitting

22 Summary & Conclusion cont d Two cases described accurately Lignin (heated grid) Black liquor (furnace) More data needed to see if parameters have general applicability More study needed for cellulose, hemicellulose and carboxylic acid parameters Need better understanding and model of secondary reactions