Mike Kryjak, Ed Lester & Joseph Perkins

Transcription

1 Computational modelling and empirical measurement of the devolatilisation and combustion of a high volatile Colombian coal Mike Kryjak, Ed Lester & Joseph Perkins European Conference on Coal Research and its Applications 1

2 RJM Specialising in innovative combustion optimisation and emissions reduction in large combustion plant worldwide Prestigious innovation award presented by the Institute of Energy (2015) Experience in lignite, coal, gas, oil and biomass Solutions driven by detailed, bespoke engineering including CFD modelling A UK-based RJM optimised unit during start-up European Conference on Coal Research and its Applications 2

3 Overview Over the years, RJM designs have begun approached IED compliance by primary measures (NO x < 200mg/Nm 6% O 2 ) This demands much increased modelling accuracy 700 Stack NO Nm 3 x 6% O 2 ) Modelling coal combustion is very difficult Many widely popular models/defaults can be improved upon significantly The above points are critical for coals beyond the traditional US/Europe fuel diet Unabated 1995 UNECE (Ist generation Low NOx burners) 2008 LCPD (plus OFA/BOFA) 2008 Kilroot (T) 2011 Ferrybridge (W) 2014 UK (T) 2014 UK (W) 2015 Rugeley (W) 2016 IED 2017 RJM Ultralow NOx Solution Commercial projects require a pragmatic approach European Conference on Coal Research and its Applications 3

4 The Study Part of a wider study into coal combustion fractions including more challenging coals in collaboration with Nottingham University PF - 3 size Dry PF DTF Pyrolysis Char particles Refire - DTF Burnout Weight loss history Weight loss history Objectives: Maximise performance of commonly used CFD software and models without UDF programming Characterise volatile release and char combustion kinetics for use in large scale CFD Colombian chosen as a baseline Low emission potential Extensive in-house experience at RJM Similar rank to many coals used in the UK European Conference on Coal Research and its Applications 4

5 Experimental Procedure Drop-tubes allow for weight loss history measurements for pyrolysis and combustion High heating rate, high temperature Particles entrained in free-fall Able to nearly replicate furnace conditions Drawbacks: Modelling required to determine particle temperature history European Conference on Coal Research and its Applications 5

6 DTF Modelling Jones J. M. et al., 1999 Historically very difficult to obtain particle temperature history The large differences in reported devolatilisation rates are Once obtained, it is very difficult to validate Measurements difficult and expensive Often inaccurate attributed to the difficulty in determining accurate particle time- Residence time is also an issue Recirculation at the inlet possible temperature profiles during devolatilisation under rapid heating conditions Fletcher H. T. and Hardesty D. R., Sandia, 1992 In a DTF, calculation of residence time can lead to errors. Also, coal particle temperature histories are difficult to determine. Calculated Results often difficult to compare temperatures can be inaccurate, while the temperature measured between studies may well be that of the gas round the particle DTI (2004) Different hardware There are a number of factors that could cause the wide variations Different modelling assumptions observed [ ]. If there are inaccuracies in determining the particle temperature [ ] this problem is believed to produce much of the variation in reported rates - Solomon P. R., Serio M. A. and Suuberg E. M., 1991 This means one often cannot use kinetics available in literature European Conference on Coal Research and its Applications 6

7 3D 2D 1m cells ( µm resolution) 3D 0.1m cells (250µm resolution) 2D European Conference on Coal Research and its Applications 7

8 Numerical Study Over 30 cases run Investigated the effect of: Mesh density Discretisation Viscosity & other gas properties Boundary layer Geometrical simplifications Particle drag Number of particles The predicted residence time is very sensitive to model setup Predictions ranged from 100 to 500ms Final residence time: ms This level of detail was found to be necessary to ensure that the model is representative European Conference on Coal Research and its Applications 8

9 Heat-up Performance 11 CFD Experimental Predicted vs. Measured DTF Radiation Intensity 1,600 Wall temperatures: 1273K 1423K 1573K Predicted Particle Temperature History 10 1,400 9 n temperature Dimens sionless radiatio 8 Particle Temp perature (K) 1,200 1, Dimensionless distance along drop-tube µm Residence Time (s) European Conference on Coal Research and its Applications 9

10 The Impact of Heat-up Heat-up - Coal heat capacity, density etc. Errors in predicted particle temperature can have significant knock-on effects Devolatilisation - Kinetics, ultimate yield, speciation, etc. Char heat-up - Char thermodynamics Burnout - Kinetics (maceral composition & morphology), deactivation Combustion extinction European Conference on Coal Research and its Applications 10

11 Particle Thermodynamics Coal particle heat capacity Merrick (1983) with second Einstein temperature correlation by Coimbra and Queiroz (1995) Coal particle density Singh and Kakati (2000) Radiation DO Assumption: particle interior temperature uniform European Conference on Coal Research and its Applications 11

12 Devolatilisation - Experimental Burn nout (DAF) Burn nout (DAF) Burn nout (DAF) Residence time (ms) Residence time (ms) Residence time (ms) 66µm 120µm 210µm Low ash content increases experimental error (issue common to all ash tracer techniques): ±1 Some samples did not reach full yield after 200ms High temperature volatile yield estimated approx. 64% based on refire data (discussed later) No great differences between temperatures or particle size European Conference on Coal Research and its Applications 12

13 Devolatilisation - CFD Burnout (D DAF) Burnout (D DAF) Burnout (DA AF) Residence Time (ms) Residence Time (ms) Residence Time (ms) Twin rates - Default Twin rates - fitted Single rate - fitted FLUENT default (Kobayashi (1977)) rates too slow Twin rates fitted to maintain temperature sensitivity at the expense of initial release Single rate model achieves a good fit at the expense of temperature sensitivity i i Note: FLUENT requires a custom subroutine to obtain variable total yield European Conference on Coal Research and its Applications 13

14 Single Burner Modelling 1 Twin rates - Kobayashi Twin rate - fitted Single rate - fitted 10 Volatile yie eld Residence time (ms) Twin rates - Kobayashi Twin rates - fitted Fitted single rate Clear differences seen Total yield identical Difference in rate affects location and concentration of release only As a pragmatic approach, both curve-fit rates appear equivalent Potential implications for stability and NO x release (beyond the scope of this study) European Conference on Coal Research and its Applications 14

15 DTF Results: Char Burnout t (DAF) Burnou Time (ms) Burnou ut (DAF) Time (ms) Burnou ut (DAF) Time (ms) 66µm 120µm 210µm Gradients look very similar for all tests Diffusion limitation? Assumption: all remaining volatile in the initial sample is released in first time step European Conference on Coal Research and its Applications 15

16 Diffusion Slow diffusion O 2 particle For the carbon to burn, it must have an O 2 molecule available O 2 has to diffuse through the boundary layer (which is a function of particle diameter) Char particle Boundary layer Diffusion can be assumed to behave the same way for all coals European Conference on Coal Research and its Applications 16

17 Diffusion Limitation Zone 1 Low temperature Low reactivity / high rank char High reactivity / Low rank char - Slow kinetic reaction - Plenty of O 2 available - Char reactivity dominates burnout - Low rank char burns much faster Zone 2 Medium temperature - Fast kinetic reaction - O 2 used up quickly - Both reactivity & O 2 supply affect burnout - Low rank char burns faster Zone 3 High temperature - Very fast kinetic reaction - O 2 in short supply, used up as soon as it arrives - O 2 supply dominates burnout - Both chars burn at the same rate European Conference on Coal Research and its Applications 17

18 Practical Implications Mitchell R.E, Hurt R.H., Baxter L.L & Hardesty D.R., 1992 (Sandia) European Conference on Coal Research and its Applications 18

19 Char Kinetics Default FLUENT reactivity used All tests match within - most much closer Further investigation is ongoing diffusion? offsetting assumptions? Area 1: Kinetic rates CO/CO 2 split Area 2: Fragmentation Porosity European Conference on Coal Research and its Applications 19

20 High Temperature Volatile Yield Not easy to measure - Requires high temperatures - and enough residence time Pragmatic workaround: - Assume all volatile released in 1 st refire - Vary yield assumption for minimum m error Result: a robust HTVM prediction (presented in previous slide) Total burnout predicti on error (dimens sionless) This value is a product of 27 separate experiments, minimising experimental error Assumed high temperature volatile yield (DAF) European Conference on Coal Research and its Applications 20

21 Conclusions A drop-tube study was performed on Colombian coal A high-resolution CFD model was created to replicate results Devolatilisation kinetics were extracted and simulated in a single burner CFD model Both the twin rates and the single rate models have limitations Single burner simulations show a significantly altered volatile profile upon fitting the kinetics, with implications on NO x and flame attachment predictions Default FLUENT char kinetics were found to match experiment well The reason for the fit is a subject of on-going research European Conference on Coal Research and its Applications 21

22 UK 500MWe Oil Flame Thank You Questions? European Conference on Coal Research and its Applications 22 BFRC 02/05/2013

23 Appendix European Conference on Coal Research and its Applications 23

24 3D vs 2D 2D K 3D K % Burnout (DAF) Residence Time (s) European Conference on Coal Research and its Applications 24

25 Single Rate Curve-fits (DAF) Burnout ( (DAF) Burnout ( (DAF) Burnout Residence Time (s) Residence Time (s) Residence Time (s) 66µm 120µm 210µm European Conference on Coal Research and its Applications 25

26 Twin Rate Curve-fits Burnout (DA AF) AF) Burnout (D AF) Burnout (D Residence Time (s) Residence Time (s) Residence Time (s) 66µm 120µm 210µm European Conference on Coal Research and its Applications 26