Simulation of liquid extraction columns using PPBLAB

ProcessNet-Jahrestagung und 30. DECHEMA-Jahrestagung der Biotechnologen 2012, 10. - 13. September 2012, Kongresszentrum Karlsruhe

Hanin Jildeh1, Matthias Mickler1, Menwer Attarakih2 and Hans-Jörg Bart1 1

TU Kaiserslautern, Lehrstuhl für Thermische Verfahrenstechnik, Centre of Mathematical and Computational Modelling 2 University of Jordan, Department of Chemical Engineering

Simulation of liquid extraction columns using PPBLAB Motivation

Mathematical Model

• Prediction of column behavior during:

• Droplet population balance model (DPBM) [1]:

• Steady state and dynamic response

 u y f d ,c y ( )    c y f d ,c y ( )    f d ,c y ( )  Qyin in    f y (d , c y ; t ) ( z  z y )        Dy  t z c y z  z  Ac        f d ,c y ( )

• Scale-up • Process synthesis, design and control

Accumulation

• Reduction of experimental effort

External Convection

Internal Convection

Axial Dispersion

Droplet Entering Rate to LLEC

Source Term

    B b (d , c y ; t , z )  D b (d , c y ; t , z )  B c (d , c y ; t , z )  D c (d , c y ; t , z )       Birth Death Birth Death      

• Saving time and money by exploring different operational conditions

Source Term

Breakage

Coalescence

• Coulaloglou and Tavlarides coalescence model (1977) [2]:

 Challenges:

4    C     (dd ')    1/3 ( d  d ') 2 ( d 2/3  d '2/3 )1/2   exp   22 x x 3      (1   )  (d  d ')    1   



Estimation of coalescence parameters

 (d , d ',d )  C1

Parameter Estimation

Case Study

Optimal model parameters  require fitting and solving the inverse problem-DPBM

1.8

1.6

• Optimization (Fig. 2&3):

1.4

 Kühni extraction column (DN 150)

1.2

1

 Test system: toluene-water

0.8

 Operating conditions:

0.6

 volumetric flow rate for o Conti. phase Qc = 170 l·h-1 o Disp. phase Qd = 7.5 l·h-1  at 140 rpm

0.4

Fig. 2: Optimization of coalescence parameters

 C1 (-)= 9.00E-03  C2 (m-2)= 1.33E+10

Fig. 3: PPBLAB [4] simulation using the optimized coalescence parameters

Fig. 1: Schematic flow chart for the mathematical algorithm

Validation

Summary • Estimation of coalescence parameters with inverse DPBM successful

• Validation I: “Steady state” (Fig. 4)  Kühni extraction column (DN 80)

• Good accord with experimental data.

 Test system: toluene-acetone-water

 OUTLOOK: Solving inverse DPBM problem for other breakage and coalescence correlations to be used with • CFD Simulation • Online prediction/control

 Operating conditions:  volumetric flow rate for o Conti. phase Qc = 40 l·h-1 o Disp. phase Qd = 48 l·h-1  at 150 rpm

References: Fig. 4: Validation of the optimized coalescence parameters using PPBLAB [4] for 4.4 m Kühni column

• Validation II: “Dynamic state” (Fig. 5)  Kühni extraction column (DN 150)  Test system: toluene-water  Operating conditions:  volumetric flow rate for o Cont. phase Qc = 170 l·h-1 o Disp. phase Qd = 11.2 l·h-1  at rpm step changes: 80 – 160 – 80 rpm

[1] M. Attarakih et al. (2006), Chem. Eng. Sci., 61(1): 113-123. [2] C. A. Coulaloglou and L. L. Tavlarides (1977), Chem. Eng. Sci., 32(11): 1289–1297. [3] D. Garthe (2006), Fluiddynamics and mass transfer of single particles and swarms of particles in extraction columns, Verlag Dr Hut, TU München. [4] M. Attarakih et al. (2012), Procedia Eng., 42: 1574– 1591.

Acknowledgment: The authors wish to thank the following foundations for the financial support

Fig. 5: Validation of the optimized coalescence parameters for dynamic step change

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