cfbc fuel flexibility: added value of advanced process optimization n3 2014 pp 48-61.pdf ·...

CFBC fuel flexibility: Added value of advanced process optimization

M. Weng, A. Omer Aixprocess GmbH, Aachen, Germany

Keywords: CFB, fuel flexibility, process optimization, plant audit, CFD simulation

Abstract—A major advantage of circulating fluidized bed combustors (CFBC) is the high flexibility according to fuel type, composition and specific properties like particle size, moisture and morphology. On the other hand, flow and transport characteristics of large scale fluidized beds are far from ideal mixing conditions. Hence, fuel flexibility design for new or existing plants is a multi-parameter optimization problem with numerous aspects from storage and feeding equipment over optimum feeding location to the effect on fuel burn-out, emissions and wear. The combination of plant audits, specific measurements (e.g. primary air nozzle pressure drop as a measure for flow distribution and bed flow pattern) and the application of advanced computational fluid dynamics are presented in this contribution. Specific fuel flexibility challenges and solutions considering

• air staging,• wear reduction and• limestone injection for direct desulphurization

are shown for 3 selected German and Polish CFB plants (150–490 MWth) and fuel compositions varying between

• coal/RDF mixture,• up to 40% substitution of coal with wood pellets and/or wood chips and• 100% biomass with different fuel specifications.

The examples demonstrate the added value of a joint process optimization approach for an efficient analysis of the complex coupling flow and transport mechanisms in CFBs. The increased insight and understanding is a major support for finding proper design and operation conditions for the application of flexible fuel compositions.

INTRODUCTION A major advantage of circulating fluidized bed combustors (CFBC) is the high flexibility according to fuel type, composition and specific properties like particle size, moisture and morphology. However, flow and transport characteristics of large scale fluidized beds are far from ideal mixing conditions. Due to the loop mode of a CFB, multiple time-scales exist from short-term pressure and velocity fluctuations to long-term shifting of bed inventory particle size distribution and miscellaneous accumulation processes. Time-scale issues are even enhanced if plant operation is not stationary due to fuel inhomogeneity or load changes as required by economic reasons.

As a consequence, fuel flexibility design for new or existing plants is a multi-parameter optimization problem with numerous aspects from storage and feeding equipment over optimum feeding location to the effect on fuel burn-out, emissions and wear.

The combination of plant audits, specific measurements (e.g. primary air nozzle pressure drop as a measure for flow distribution and bed flow pattern) and the application of advanced computational fluid dynamics (CFD) for reacting dense particle flows is presented in this contribution.

South African Journal of Chemical Engineering, vol. 19, 2014, no. 3, pp 48-61 48

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NUMERICAL SIMULATION IN FLUIDIZED BEDS AND DENSE PARTICLE FLOWS The CFD simulations make use of the Multiphase Particle-in-Cell (MP-PIC) method.3 The CPFD method solves the fluid and particle momentum equations in three dimensions. The fluid is described by the Navier-Stokes equation in bi-directional coupling with the discrete particles. The MP-PIC numerical scheme is a Lagrangian description of particle motion described by ordinary differential equations with back-coupling to the fluid. This Computational Particle Fluid Dynamics (CPFD) solution as applied in the commercially available software Barracuda VR® is aimed at solving industrial problems, which are generally physically large systems. In the CPFD scheme, a numerical particle is defined where particles are grouped with the same properties (species, size, density, etc.). The numerical particle is an approximation similar to the numerical finite control volume where a spatial region has a single fluid property. Using numerical particles, large commercial systems containing billions of particles can be analyzed using only millions of numerical particles. The simulation is strictly transient, thus accounting for the inherently fluctuating character of flows with high solid volume fractions.

Governing equations The volume average two-phase continuity equation for the fluid (here written without interphase mass transfer) is + ∇ ∙ = 0 (1)

with fluid velocity uf and fluid volume fraction θf. The volume average two-phase incompressible momentum equation for the fluid is + ∇ ∙ = − ∇ − + + ∇ ∙ (2)

where ρf is fluid density, p fluid pressure, τ the macroscopic fluid stress tensor, and g the gravitational acceleration. F is the rate of momentum exchange per volume between the fluid and particles phases.

The particle acceleration is = − − ∇ + − ∇ ∙ (3)

where up the particle velocity, ρp particle density and τp particle normal stress. The terms represent acceleration due to drag, pressure gradient, gravity and inter-particle normal stress gradient. Particle properties are mapped to and from the Eulerian grid. The interpolation operator is the product of interpolation operators in the three orthogonal directions.

The interphase drag coefficient is = (4)

where µf is the fluid viscosity, r is the particle radius and Cd , and the drag correlation from Wen and Yu.6

Particle-to-particle collisions are modeled by a particle normal stress expression. The particle stress is derived from the particle volume fraction which is in turn calculated from particle volume mapped to the grid. The particle normal stress model used here is

= , (8)

where Ps is a material parameter, β a model parameter in the recommended range of 2 ≤ β ≤ 51, is particle close pack volume fraction and ε is a small number of the order of 10-7 to remove

the singularity. The close-pack limit is somewhat arbitrary and depends on size, shape and


ordering of the particles. Therefore the solution method allows the particle volume fraction, at times, to slightly exceed close-pack which is physically possible considering that shifting or rearranging of granular materials may occur. The particle normal stress is mapped to discrete particles. Because particles have sub-grid (no grid) behavior, the application of the normal stress gradient to a discrete particle accounts for the particle properties and whether the particle is moving with or against the stress gradient.

The gas phase turbulence is taken into account by a Large Eddy model with a Smagorinsky model based on a coarse sub-grid allowing for time steps in a millisecond order of magnitude. However, there are currently no validated turbulence models for dense particle flow. Large density and size particles act as large eddies of momentum transfer while gas flow around close pack particles produces small sub-grid eddies and dissipation.4

Modeling of coal and secondary fuel combustion The homogeneous and heterogeneous fuel reactions are represented by a reduced mechanism (see Table 1). The equilibrium reactions are split into single equations for forward and backward reactions. All kinetic reactions rates are taken from approved references.2,5,7 Simplifying the complex heat and mass transfer in partially porous particles, moisture and volatile fuel contents are assumed as gaseous inlet streams at the same positions as the solid fuel feed. The model error is considered to be small since drying and volatiles evaporation are very fast at fluidized bed conditions with relatively small fuel particles. Variation simulations with modeling of a distinct volatiles release rate from solid particles showed a low sensitivity for small fuel particles.

Table 1. Stoichiometric equations for the reduced combustion mechanism

Steam gasification C(s) + H2O ↔ CO + H2

CO2 gasification C(s) + CO2 ↔ 2CO

Combustion λC(s) + O2 → 2(λ-1)CO + (2-λ)CO2

Water gas shift CO + H2O ↔ CO2 + H2

Volatile combustion CxHyOz + αO2 → βCO2 + γH2

CO combustion 2CO + O2 → 2CO2

Circulating fluidized bed plant model A full CFB loop model including combustion chamber, cyclone, sealpot, fluidized bed cooler and solids recycle line can principally be modeled by transient MP-PIC simulation, but for large models, a combined CFD and process model may be more economic. For the given examples with focus on combustion and wear phenomena in the combustion chamber, CFD was performed for the CFB riser only. Fractional cyclone separation and heat withdrawal in the fluidized bed cooler were calculated by zero-dimensional balancing models and coupled to the boundary conditions of the combustion chamber outlet (exit to cyclones) and recycle line inlets.

SELECTED EXAMPLES FOR PROCESS OPTIMIZATION

CFB Wuppertal-Elberfeld, Germany The Elberfeld plant consists of 2×137 MWth CFB boilers (Fig. 2) manufactured by L+C Steinmüller and was commissioned in 1991. Actual data is given in Table 2. The operational mode depends on the district steam and heat demand for industrial and communal use and the market price for electricity. Due to the German power market regulations for renewable energies, the plant undergoes daily load alternations between 60–100% load with an actual ratio of partial to full load of 3:1. The specific requirement is a high plant flexibility and fast load change velocities.


Mdamaboilerdeforveloc

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Firing Steam generaFuel

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Main motivatage causing r roof in dirermations sugcities. he damages ht from norm

mber (“high ibution had The operati

ations of 102damages coune root causreleasing a m

ulent, it is weomplete mixisurement for

wall indicatsure drop) ina rather unif

ator

put meters

emperature ours -XII/2013ation hours

tion for a coan unplann

ect line abovggest the im

occurred afmal operatio

bed”) to Δbeen adopte

ional trials w%. As a supeuld not be eae assumptio

massive primell known thing of any m

r the Elberfelting some pan the sectionform distribu

Table 2. E

AtmospheOnce throuHard coal,input after2×137 MWHP 2×47,2LP 2×42,8 260°C

3 149,108 (B~ 4,500–7,0

Figure 2

omprehensivned shut-dowve the RDF fumpact of lar

fter a series on characteriΔp=150 mbaed from the were superimerposition ofasily identifien for the loc

mary air streahat lateral di

mal-distributild plant. Theartial blockin

of RDF inleution and a c

Elberfeld CFB

eric circulatingugh (system B, residuals derr retrofit

W 2 kg/s, 535°C,

kg/s, 535°C, 4

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ve process awn, revealingfuel inlet locarge particles

of former ized by Δp =ar (“low bed

original casmposed by df a set of opeed from operally restricte

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plant data

g fluidized bedBenson) rived fuel (RD

201 bar 46 bar

B32), ~ 80% fu

FB plant

nalysis of thg a very speated in the bs being shot

operational = 175 mbar d”). In anotse “S1” to a daily load cherational conrational obseed roof wear urnace. Altholarge scale f

k. Fig. 4 showon of high nooticeable wea

y the measurtation across

d (system LUR

DF) up to 25% t

ll load

he Elberfeldecific wear p

bottom left cot against th

trials with cthroughout ther trial, thnearly equaanges with editions, the m

ervations. was a partly

ough the fluiluidized bed

ws the result ozzle pressuar (as indicaements showthe distribu

RGI)

thermal

d CFB was apattern undorner (Fig. 3

he roof with

changing ththe fluidize

the secondaralized distribeventual ovemain cause f

y worn distridization is hds is not suffof the nozzl

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ons were perboiler load). ight). Singlebling big parluation (Fig.

rend of diffeas observeds a simple lbut with low

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the wear sicondary air t

height, secole speed (left the local flubed chambe

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ndary t) andue gas er. shows in the

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was slightly e, no more sp

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fluidized bed eeler AFBC tec

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side of the fus under variaribution and of velocities, eristics. The influence onrespective opon on the ceantly good, m

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simulated in re, solids anses the modeormance, pot

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gas temperatuets with non-s

as velocity distwith non-sym

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tribution (m/smmetrical seco

ture contentlow is kept cl conditions

in reason is tnded fuel prts volatile a

wer temperatuwood chips mg in partial mtor nozzles ured and prcities, tempeation.

on (°C): A wooecondary air d

s): A wood pendary air dist

t (B) results constant. Wh(C, 75% seco

the high momarticles to t

and coke bur

ures (B) is mmoisture (seemode leads t(see Fig. 10

redicted emieratures, emi

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ellets, B wet wribution

in significanhen the seconondary air frmentum fromthe back sidrn-out in the

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wet wood chip

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242 MWth CFActual data is

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Figure 11. Mo

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and partial

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locations wechute, mixing

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Figur

atilization ofeneath 5%. Tting in high r

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Figure 12. F

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re 13. Fuel par

f biomass is The very largrates of coke

(100% mixin

Fuel Composit

in composization behaoal particles arly over 30 s

rticles colored

considerablyge biomass p

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tion and Size

sition and savior. In Figuvolatilizatio

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e shifting voatures occur re still well b100% coal.

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unfavorable b

Fi

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CONCLUSION The examples demonstrate the added value of a joint process optimization approach for an efficient analysis of the complex coupling flow and transport mechanisms in CFB’s. The increased insight and understanding is a major support for finding proper design and operation conditions for the application of flexible fuel compositions. Although CFB simulation is now an established and central tool for understanding the complex interactions of gas/solids kinematics, heat transfer and combustion reactions, plant audit and verification of the actual plant state are crucial procedures for process evaluation and optimization. Analysis of distribution grid nozzle wear as the primary indicator for fluidization quality and instantaneous and averaged values from the process control system are required for plant balancing, definition of any model boundary conditions and interpretation of both measured values and simulation result.

REFERENCES 1. Auzerais, F.M., Jackson, R., Russel, W.B. 1988. Journal of Fluid Mechanics 195.2. Bustamante, F., Enick, R. M., Cugini, A., Killmeyer, R. P., Howard, B. H., Rothenberger, K.

S., Ciocco, M. V., Morreale, B. D., Chattopadhyay, S. and Shi, S. 2004. AIChE J., 50:1028–41.3. O’Rourke, P.J., Zhao, P., Snider, D.M. 2009. Chemical Engineering Science, 64.4. Snider, D. M. 2001. Journal of Computational Physics, 170:523-49.5. Syamlal, M., Bissett, L.A. 1992. DOE/METC--92/4108, DE92 001111.6. Wen, C.Y. Yu, Y.H. 1996. Chem. Eng. Progr. Symp., Ser., 62:100-1107. Yoon, H., Wei, J. & Denn, M.M. 1978. AIChE J., 24:5


cfbc fuel flexibility: added value of advanced process optimization n3 2014 pp 48-61.pdf ·...

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