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Dynamic Modeling of a BatchBioreactor for Trans-esterification of

Waste Vegetable OilNabeel Adeyemi

Department of Mechanical Engineering,

Faculty of Engineering,

International Islamic University Malaysia

Malaysia

Supervisor: Prof A.K.M. Mohiuddin (Mechanical Eng Dept, IIUM),

Co-Supervisor: Assoc.Prof Dr Tariq Jameel (BiotechnologyEngineering Dept, IIUM)

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Objectives

This primary aim is this work is to model thetransesterification of waste cooking oil (WVO)using Computational Fluid dynamics (CFD)techniques where reaction and flow in a reactor

are simultaneously considered

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Introduction Biodiesel Production based on Food Oils.

Fuel vsFood debate Alternative raw material

WCO-thermally-degraded Food Oils(>17hrs domestic/ industrial use at 60-90C or more).

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Introduction (Contd)

Factor affecting biodiesel production temperature, ratio of alcohol to oil,

catalyst type and amount, FFA and

water content and mixing intensity

Optimization of biodiesel productioncarried out in lab flask accounting

ONLY for the kinetics related effect.

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Problem Statement

Yield in WCO transesterification is about80-85% in batch reactors due to masstransfer and kinetics-related limitations.

The reason alluded to this is with littlereference to physical condition ofreactor, where the mixing of the

reactant can be significantly improvedduring the reaction

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RESEARCH PHILOSOPHY

If the hydrodynamics in bioreactor areconsidered and accounted for, alongthe reaction kinetics, reaction

parameters can be easily optimized forvarying process condition in reactordesign using CFD (Multiphysics

approach)

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Coupled multiphysicsphenomena

Momentum(Navier-Stokes Equations)

Energy(Convection and Conduction, Heat transfer)

Mass(Convection and Diffusion, Reaction)

Velocity, pressure

Temperature

Density, viscosityThermal conductivityHeat capacityReaction rate

reaction rate

Concentration

1 2

3

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Previous Milestone

Concluded WVO transesterification using

Taguchi L9 orthogonal array design

Formulated a kinetic model for the reaction

Simulated the flow in 2 D axi-symmetrical mode

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METHODOLOGY

Kinetic model Nonlinear regression (Least square fitting method)

CFD simulation-Multiple Reference frame (MRF)

model 3D (RANS k-e, RSM and LES) turbulent models Exploitation of P.I.V. measurements:

data analysis (RANS k-e, RSM and LES) turbulent

models

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ResultsKinetic Model for Transesterification

TRI + OHDG + ES (1)

r1f=k1f*[TRI]*[OH]; r1r=k1r*[DI]*[ES]

d[TRI]/dt= k1f*[TRI]*[OH]-k1r*[DI]*[ES]

DG + OH MG + ES (2)

r2f=k1f*[DI]*[OH] r2r=-k1r*[MG]*[ES]

d[DI]/dt = =k1f*[TRI]*[OH]-k1r*[DI]*[ES]-k2f[DG][OH]+k2r*[MG]*[ES]

MG +OH tr + ES (3)

r3f=k3f*[MG]*[OH], r3r=k3r*[tr]*[ES]

d[MG]/dt = =k3f*[MG]*[OH]-k3r*[tr]*[ES]+k2f[DG]*[OH]-k2r*[MG]*[ES]

d[ES]/dt = k1f*[TRI]*[OH]-k1r*[DI]*[ES]]-k2f[DG][OH]+k2r*[MG]*[ES]+k3f*[MG]*[OH]-k3r*[tr]*[ES]

With initial value of TG=0.49746, DG=0.82212, MG=0.08876, GLY=0.01378

Equation 1-3 solved

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Select Initial value for preliminary fitting

specie concentration(TG, DG, MG)

k values

Compare between the experimental data and the

kinetic model

estimated using non linear regression by

minimizing the standard

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Effect of impeller type, position and speed onyield: Rushton Impeller

Figure 2: FAME wt (%) at (a) 60 (b) 65 (c) 70 C for N = 600, 650, 700 rpm and

IBC = 20, 25, 30 mm for Rushton impeller in baffled reactor

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Figure 3: FAME wt (%) at (a) 60 (b) 65 (c) 70 C for N = 600, 650, 700 rpm and IBC = 20, 25, 30 mm

for Rushton impeller in unbaffled reactor

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Effect of impeller type, position and speed onyield: Elephant ear Impeller

Figure 4: FAME wt (%) at (a) 60 (b) 65 (c) 70 C for N = 600, 650, 700 rpm and IBC = 20, 25, 30 mm for Elephant

Ear impeller in baffled reactor

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Figure 5: FAME wt (%) at (a) 60 (b) 65 (c) 70 C for N = 600, 650, 700 rpm and IBC = 20, 25, 30 mmfor Elephant Ear impeller in unbaffled reactor

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Table 2: S/N ratio of yield

Temp(C)

Speed (rpm)IBC

(mm)

Rushton

unbaffled

S/N ratio

Elephant Ear

(Baffled)

S/N ratio

60 600 20 38.86 39.25

60 650 25 39.20 39.3760 700 30 39.09 39.22

65 600 25 39.05 39.23

65 650 30 39.10 39.23

65 700 20 38.86 39.00

70 600 30 39.25 39.11

70 650 20 39.05 39.18

70 700 25 39.12 39.31

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Linear relationship of temperature, speed and

Impeller bottom distance

60 65 600 650 20 25

Yield using Rushton Impeller39.0624 0.0146t 0.0604t 0.0111S 0.0509S 0.1387I 0.0578I

60 65 600 650 20 25

Yield using Elephant ear Impeller

39.06 0.13t 0.12t 0.29S 0.07S 0.27I 0.21I

(1)

(2)

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Table 3: Statistical correlation of temperature; Speed and IBC to yieldand peak yield time

Rushton (Peak Yield time) Elephant ear (Peak Yield time)

SE Coeff T p SE Coeff T p

Constant 2.07 9.13 0.01 0.42 218.57 0

T 3.83 2.05 0.18 0.77 -0.57 0.63

S 3.83 -1.74 0.22 0.77 -0.72 0.55

IBC 3.83 -2.74 0.11 0.77 1.34 0.31

T*S 5.74 -2.49 0.13 1.16 1.4 0.30

T*ID 5.74 0.87 0.48 1.16 -0.82 0.50

S*IBC 5.74 1.87 0.20 1.16 -0.03 0.98

p-value < 0.05 for all parameters

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Nonparametric

model

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Figure 5: surface plot of FAME weight yield as functionof peak yield time and temperature

60

65

70

5

10

15400

410

420

430

440

450

temp (C)peak yield time (min)

F

AME

wt(g)

41

42

42

43

43

44

44

302

5201

5105

peakyieldtime(min)

60616263646566676869

temp

(C)

350

350

360

360

370

370

380

380

390

390

400

400

410

410

FAMEwt(g)

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Figure 6: surface plot of FAME peak yield time as afunction of IBC distance and speed

20

25

30 600 620640

660 680700

0

5

10

15

20

25

30

35

speed (rpm)distance (mm)

peakyieldtime(min)

5

10

15

20

25

30

600

650

70020 22 24 26 28 30

4

6

8

10

12

14

16

speed (rpm)

distance (mm)

peakyieldtime(min)

5

6

7

8

9

10

11

12

13

14

15

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Figure 7: surface plot of FAME weight yield as a function of

temperature and speed

600620640660680

700

60

65

70

350

360

370

380

390

400

410

temperature (C)

speed (rpm)

FAME

wt(g)

355

360

365

370

375

380

385

390

395

400

405

60 62 64 66 68 70600

650

700

400

410

420

430

440

450

speed (rpm)

temp (C)

FAME

wt(g)

41

41

42

42

43

43

44

44

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PIV

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Reactor Property Dimension

impeller bottom clearance, C(mm) 0.11T-0.27T

Height, H(mm) 150

Tank diameter, T(mm) 130

Total Liquid Height, L(mm) 0.34T

Impeller Diameter, D(mm) 0.23T

Table 1: Physical dimension of biodiesel Reactor

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RESULTS (Contd)

Figure 1. Simulated mean tangential, radial and axial velocity at impellerbottom clearance, C=0.11T, 0.15T, 0.19T, 0.23Tand 0.27Tfor unbaffled

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Figure 2: Time average velocity field (m/s) for full tank using Rushton

Impeller (unbaffled)

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Figure 3: Time average velocity field (m/s) for full tank using Rushton

Impeller (unbaffled)

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0

0.20.4

0.6

0.8

11.2

1.4

1.6

1.8

0.1 0.0 0.8 0.9 1.0

r/D

U/Umax(m/s) K-E

LES

RSM

PIV

Figure 4: Comparison of mean Tangential velocity forRAN (k-e), LES, RSM models at 30 mm from shaft centre

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Physical Model

Bioreactor with Rushton Impeller

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0 100 200 300 400 500 600 700 800 900 1000 1100 1200pix

0

100

200

300

400

500

600

700

800

900

1000

pix

Statistics vector map: Vector Statistics, 3931 vectors (1209)

Size: 12801024 (0,0)

Figure 5: Velocity (vectors) of stirred flow with20m PSP using Rushton impeller at 600 rpm

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0 100 200 300 400 500 600 700 800 900 1000 1100 1200pix

0

100

200

300

400

500

600

700

800

900

1000

pix

Statistics vector map: Vector Statistics, 3931 vectors (1209)

Size: 12801024 (0,0)

Figure 6: Time average velocity field (m/s) for full tank usingElephant Ear (unbaffled) (a) CFD (b) PIV

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Figure 8: Time average velocity field (m/s) for fulltank (a) CFD (b) PIV

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Conclusion

Goals achieve

Effect impeller on FAME yield

Non-parametric model using temperature, speed and

bottom distance to model FAME yield and peak time

PIV evaluation of the velocity structure by Rushton and

Elephant ear

Local rate of energy dissipation

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Future Thrust

PIV resolution to determine kolmorogov scale

for spatial variability of parameter during mixing

(macro,meso, or micro) mixing Time average shearing field (s-1) computed by Local rate of energy dissipation

Coupling of reaction and flow model

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Thank you

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.u 0 Tu

(u. )u . I+ ( u ( u) ) Fpt

Governing EquationsFor the fluid flow, the governing equations (continuity and momentum)

for incompressible flow were used;

F is source term dependent on the velocity component and u is

the absolute velocity, which can be calculated by

The variables velocity and stress variables are decomposed into time averageand fluctuating component which are substituted into the governing equationsfor incompressible flow to give the RANS

to the momentum equation called the Reynolds stress tensor

Decomposing the variables in Navier-Stokes equation yields an additional term,

i ju u

u r v r ur r r

TURBULENT MODEL

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This gives a system of equations of 13 unknown variables and 9 equations and istherefore not closed

2

3

ji

i j t ij

j i

uuu u

x x

The closure of the Reynolds stress term results in the development of turbulencemodels such as the standard k-e, RNG k-e, realizable k-e and Reynolds stress model

Relationship between the Reynolds stresses to the mean flow velocity gradientsand can be expressed as

2

t

kC

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2

1. U. = U U.

2

TTT

t

2 2

1. U. = U U.

1 12

TT

C CTt

kthe turbulent kinetic energy, defined as 12

i ju u

1 21.3, 1, 1.44, 1.92, 0.09

kC C C

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Cylindrical Coordinates

rrr

r

rz

rrr

r

gV

rz

VV

rrV

rrr

r

P

z

VV

r

VV

r

V

r

VV

t

V

22

2

2

2

2

2

211

zzzzz

zzz

rz g

z

VV

rr

Vr

rrz

P

z

VV

VV

r

VV

t

V

2

2

2

2

2

11

gV

rz

VV

r

rV

rrr

P

rz

VV

r

VVV

r

V

r

VV

t

V

r

zr

r

22

2

2

2

2

211

1

Centrifugal force

Coriolis force