Modelling of Biomass PyrolysisSamreen Hameed, Adhirath Wagh, Abhishek Sharma, Milin Shah, 

Ranjeet Utikar and Vishnu Pareek13/09/2017

A global university  Perth  |  Kalgoorlie | Dubai  |  Malaysia  |  Singapore

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Biomass

Thermo-chemical conversion

Combustion ( in presence of

excess air)Heat energy

Gasification(in presence of partial

air and steam)Syngas and Fuel gas

Pyrolysis(in inert atmosphere)

Bio-oil, Bio-char and Fuel gas

Hydrothermal liquefaction

(in presence of high pressure water)

Bio-oil

Bio-chemical conversion

Hydrolysis-Fermentation

Bioethanol

Anaerobic digestion Biogas

Biomass Conversion Processes

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Pyrolysis Process Technologies

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Multi-Scale Modelling

Kinetic Model

Particle Model

Cold Flow Model

Reactor Model

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Particle Model

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Granular Flow Modelling

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Circulating Fluidized Bed (CFB)

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Gas-Solid Flow in Riser

Radial profile Axial profile

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Effect of Boundary Condition

Inlet in experiments

Inlet configuration in 2D CFD model

Radial profile predicted by 2D CFD model

(Benyahia et al., 2000)

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Effect of Boundary Condition

Inlet config.-1 (BC-A) Inlet config.-2 (BC-B) Inlet config.-3 (BC-C)

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Effect of Boundary ConditionInlet config.-1 (BC-A) Inlet config.-2 (BC-B) Inlet config.-3 (BC-C)

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Effect of Boundary Condition

Inlet config.-1 (BC-A) Inlet config.-2 (BC-B) Inlet config.-3 (BC-C)

Neither of these BCs represents true experimental kinetic energy of phases at inlet

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Circulating fluidized bed

So where is the challenge?

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Need for Multiscale Drag Model

Formation of Clusters

Fluid flows around the clusters without

penetration

Significant Drag Reduction

How to model the effect of

clusters

Wen-Yu and Ergun drag models: Do not

capture effect of clusters

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Gidaspow’s Drag Model

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Derivation of Multiscale Drag Models: Available Approaches

SGSFine grid periodic simulations

Derive closure models for coarse grid simulations

Gas-solid interactions calculated by local flow conditions + empirical cluster diameter correlationsEMMS

DNSGas-particle interactions are resolved at surface of particles

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EMMS Framework

c = cluster; f = dilute phase; and i = dense phase; ε = voidage; Us = superficial slip velocity; mc, mf and mi = number of particles per unit volume in cluster, dilute and dense phases; Fc, Ff and Fi = force per unit volume in cluster, dilute and dense phases

Individual particles in dilute gas phase

Particles in dense cluster phase

clusters as a large particle in gas phase

Three pseudo phases

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EMMS Framework: Drag Forces

; ; f = cluster fraction or fraction of volume occupied by clusters; dcl= cluster diameter; Usc, Usf = superficial slip velocities in dense and dilute phase; Usi = slip velocity between dilute phase and dense cluster

,.

,1 0.15 , .

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EMMS and Gidaspow Model

Low solid flux High solid flux

Particle dia. (dp) 54 µm 76 µm

Density 930 kg/m3 1712 kg/m3

Solid mass flux (Gs) 14.3 kg/m2s 489 kg/m2s

Superficial gas velocity (Ug) 1.52 m/s 5.2 m/s

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Verification of EMMS: Lattice Boltzmann

Particles forming a cluster – Known cluster size, cluster

fraction and solid volume fractions

Particles forming a random

configuration

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CFD Predictions at Low Solids Flux

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CFD Predictions at High Solids Flux

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CFD Model Parameter

Properties Cellulose Sand

Density (kg/m3) 1500 2560

Particle Diameter (μm) 250-900 440

Restitution coefficient 0.97 0.97

Angle of internal friction 35

Cellulose to Sand Ratio 0.1-1.0

Umf (sand) (m/s) 0.145

Ug/Umf 1-3

Air Inlet

0.10

2m

0.25

5m

0.102m

Air Outlet

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Sample Results

a) Cellulose b) Sand

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Volume Fraction Profiles

a) Air b) Cellulose c) Sand

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Segregation of Sand and Biomass

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Model Predictions

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Pyrolysis Process Model

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Kinetics of Pyrolysis Process

• Experimental TGA data at heating rates between 5 to 250 K/min has been obtained for three different particle sizes (<45, 75 to 106 and 300 to 425 μm)

• Imaging intermediate and final solid residues formed during pyrolysis to get an insight into volatilization process

• Formation of open pore structure under different heating rates affects the values of frequency factor and its dependence on temperature

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Kinetics of Pyrolysis Process

• Biomass is a heterogeneous solid material and its pyrolysis is modelled using distributed activation energy model (DAEM).

1 exp exp

• Here, ‘α’ is the fraction of volatiles released and is modelled as a linear combination of volatilization of three pseudo components having weight fraction Ci.

• The values of m and Ci have been adjusted to fit the volatilization profile of pine. The activation energies have not been adjusted

Experimental and modelled data

Predicted volatilization profile for pine at 1000 K/s

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Pyrolysis Process

1: K-Tron Gravimetric feeder2: Gas pre-heater3: Screw Feeder4: Fluidized Bed reactor5: Cyclones6: Char collection Vessel7: Fixed Bed reactor followed by three heat exchangers

4

5

6

1

7

3

2