2. CARPT Calibration Issues
Poster 1
Bad Reconstruction
Calibration Curve
Counts
Distance (cms)
Photo Peak
Compton
0.89 Mev
1.12 Mev
Full Spectrum
Proposed Solution to HPBCR
c2 Approach
Good Reconstruction
Acquire Photopeak
Good
Calibration Curve

3. Gas Liquid Studies in Stirred Tank Reactor
Poster 2
New techniques in CT implementation result in better reconstruction!!! How ???
Cross sectional gas holdup distributions (at different axial planes) in a Stirred Tank Reactor (for the first time!!)
Detailed local fluid dynamic information in gas-liquid flows in stirred tank (for first time!!)
N=100 rpm, QG=7.5 lit/min, Z=10.0 cms
2.3mm
1.0mm
0.8mm
250 mm
150 mm

4. Next Presentation

5. CHEMICAL REACTION ENGINEERING LABORATORY
Peter Spicka
research associate at CREL since 2001
bubble columns, trickle beds
CT & CARPT experimental studies and CFD simulations
Poster I: Review of CARPT/CT Experimental Database for Bubble Columns Operating in the Churn-Turbulent Regime
Contributors:
Jinwen Chen, Abdenour Kemoun, Sailesh Kumar, Shadi Saberi, Sujatha Degaleesan, Puneet Gupta, Booncheng Ong, Shantanu Roy, Qingi H. Wang
Poster II:Effect of Nozzle Orientation of the Cross Sparger and Pressure on Bubble Column Hydrodynamics
CHEMICAL REACTION ENGINEERING LABORATORY

6. Review of CARPT/CT Experimental Database for Bubble Columns Operating in the Churn-Turbulent Regime
Different effects studied:
gas superficial velocity, liquid properties, pressure
column diameter, internals, gas distributor and solids concentration
DOE project ( )
Participants: Air Products, Ohio State University, Sandia National Laboratories and Washington University
Objectives:
effect of specific variables on observed flow patterns in bubble columns
engineering type models for flow and mixing in bubble columns
data for gas hold-up, velocity and turbulence profiles for validation of CFD codes
Scale up of bubble columns:
correlations for liquid centerline velocity, gas holdup, holdup and liquid velocity radial profiles and eddy diffusivities

7. Effect of Nozzle Orientation of the Cross Sparger and Pressure on Bubble Column Hydrodynamics
Nozzle orientation of the sparger
important design issue since it may affect the length of the flow development region (Schollenberger et al., 2000)
sparse information in the available literature
Goals
to quantitatively determine the variation of gas hold-up, liquid recirculation and mixing due to variations in nozzle orientation and pressure
to compare the exp. data with 1-D recirculation model due to Kumar (1992)
Gas holdup Liquid velocity profile
Experiment
6” I.D. stainless steel column
cross-sparger, two nozzle orientations: facing upward and downward
Air-water system
Pressure: 1 bar and 4 bars
UG= 5 cm/s (only CT) and 20 cm/s

8. Next Presentation

9. Solid tracer: Catalyst particles doped with an oxide of Mn56
Development of Improved Engineering Models for Flow, Mixing and Transport in Bubble Columns (Review of Phenomenological Model accomplished in CREL during )
Tracer experiments during Methanol, Fischer-Tropsch and Dimethyl Ether Synthesis
Gas tracer: Ar41
Liquid tracer: Powdered oxide of Mn56 (Mn2O3) suspended in the heat transfer oil
Solid tracer: Catalyst particles doped with an oxide of Mn56

10. Contents Suitability of the Axial Dispersion Model
One Dimensional Recirculation Model
One Dimensional Recycle with Cross Flow and Dispersion Model
Two-Dimensional Convection-Diffusion Model
Gas Phase Recirculation and Dispersion Model
Scale-up Strategy
Comparison of experimental and simulated tracer responses
What is the next step?

11. Radioactive Tracer Studies in the AFDU Reactor during Dimethyl Ether (DME) Synthesis
cross-section along with scintillation detectors and their lead shielding

12. Schematic of the reactor compartmentalization for the gas-liquid mixing model with interphase mass transfer (Gupta, 2001)

13. Implementation of Breakup and Coalescence Models into CFD of Bubble Column FlowsG L
The engineering models needs input
Only Eulerian model seems feasible for churn turbulent flow regime
In churn turbulent regime, bubble size is widely distributed; the mean bubble diameter seems to be the simplest assumption
However, it is difficult to choose the “right” bubble diameter without going through tedious trial-and-error procedure
If one try to change to another column or operation condition, the “right” bubble diameter normally may not work any more
We need to predict rather than input bubble diameter, we had better predict it locally!
Good afternoon!
Ladies & gentlemen, today I would like to discuss some of our efforts in Implementation of Breakup and Coalescence Models into CFD of Bubble Column Flows.
???

14. Next Presentation

15. CFD Modeling of Bubble Column Flows
(Review of CFD activities in CREL )
Gas Outlet
Gas Inlet
Bubbly flow regime
Gas Outlet
Contributors:
Dr. M. P. Dudukovic
Dr. M. H. Al-Dahhan
Dr. S. Kumar
Dr. Y. Pan
Dr. M. Rafique
Mr. P. Chen
Compiled by: M. Rafique
Research Associate
Ph.D. (Fluid Mechanics), INPL, France
Acknowledgement:
DOE Contract: DE FC PC 95051
Outline:
Hydrodynamics of bubble columns
Eulerian-Eulerian Two-Fluid Model
Algebraic Slip Mixture Model (ASMM)
Hydrodynamics of (passive) tracers (gas/liquid) in bubble column flows
Implementation of the Bubble Population Balance in CFD
Gas Inlet
Churn turbulent regime

16. 2D & 3D dynamic simulations
D=15.2 cm
0.2 cm
L=10D =152 cm Ug
=1cm/s
width
10 cm 2D 3D
Mesh system
&
Gas holdup contours

17. Numerical particle tracking (calculation of turbulent diffusivities)
Numerical (liquid) Tracer Study
Numerical particle tracking
(calculation of turbulent diffusivities)
0 sec sec sec sec sec sec

18. Meso-scale modeling of bubble column flows
M. Rafique, M. H. Al-Dahhan, M. P. Dudukovic
Objective:
To simulate the bubble column hydrodynamics by resolving meso-scale flow structures through grid refinement.
To study the effect of attraction and repulsion forces on the hydrodynamics
D=15.2 cm
D=15.2 cm
width
L =110 cm
Fine grid (0.2x0.25 cm)
Coarse grid (0.5x0.5 cm)
10 cm
10 cm Ug Ug
=1cm/s
=1cm/s
0.2 cm

19. Instantaneous contours of gas volume fraction
Coarse grid
Cd+Cv
Fine grid
Cd+Cv
Fine grid
Cd+Cv+Catt

20. Next Presentation

21. Measurement of Bubble Dynamics in Bubble
CHEMICAL REACTION ENGINEERING LABORATORY
Measurement of Bubble Dynamics in Bubble
Columns Using Four-point Optical Probe
J. L. Xue, M. H. Al-Dahhan & M. P. Dudukovic
In cooperation with
Robert F. Mudde
Delft University of Technology, The Netherlands
Chemical Reaction Engineering Laboratory (CREL)
Oct. 24, 2002
Optical Fiber Probes for Bubble Dynamics
Measurement in Bubble Columns
J. L. Xue, M. H. Al-Dahhan & M. P. Dudukovic
In cooperation with
Robert F. Mudde
Delft University of Technology, The Netherlands
Chemical Reaction Engineering Laboratory (CREL)
November, 2001

22. CHEMICAL REACTION ENGINEERING LABORATORY
Motivation
bubble dynamics, i.e. bubble size distribution, bubble velocity distribution, specific interfacial area and gas holdup are among the key parameters that affect the hydrodynamics in bubble columns.
Measurement of bubble dynamics is difficult, especially in churn-turbulent flows.
Non-invasive techniques, e.g. video imaging techniques, are limited to 2-D transparent columns. and can not be used in real 3-D systems which are opaque due to high volume fraction of the dispersed phase.
Optical probes can be applied in practical 3-D systems. The measurements of bubble dynamics by two-point probes are not reliable. Four-point optical probe was adopted in this research to measure the bubble dynamics in bubble columns.

23. CHEMICAL REACTION ENGINEERING LABORATORY
The Configuration of the Four-point Optical Probe
With a new data processing algorithm, it can measure:
Bubble velocity vector
Bubble size
Specific interfacial area
Gas holdup
Side view
Bottom view

24. Next Presentation

25. CHEMICAL REACTION ENGINEERING LABORATORY
Computed Tomography in
Slurry Bubble Column Reactors
Ashfaq Shaikh, M.H. Al-Dahhan
Acknowledgements: DE-FG-26-99FT40594
CREL Annual Meeting
October 24, 2002

26. CHEMICAL REACTION ENGINEERING LABORATORY
Evaluate the CT/Overall gas holdup algorithm
System: Therminol LT-air-glass beads (150 m)
Mimic fluid
Single source CT
Two-phase systems
Three-phase systems
One equation, two unknowns
Need one more equation
CT/Overall gas holdup (Rados, 2002)
Sensitivity analysis
Assumptions in CT/Overall gas holdup procedure has been critically examined

27. Next Presentation

28. L.A. Tarca, B.P.A. Grandjean, F. Larachi
Assessing and Reinforcing the Phenomenological Consistency of Multiphase Flow Artificial Neural Network Correlations
L.A. Tarca, B.P.A. Grandjean, F. Larachi
Classically built ANN models may be phenomenological consistent in vicinity of some data points but not in the whole input space  Large prediction errors mL
Piché et al. (2001) [3]
Counter-current packed bed

29. Pressure drop in counter-current packed beds
Using phenomenological error (PCE) of the trained models we guide a Genetic Algorithm search for pertinent dimensionless numbers to predict a reactor characteristic
Pressure drop in counter-current packed beds
Phenomenological Consistency Error
Phenomenological and prediction error decrease by combining multiple “good” ANNs … bm 1 S 2 3 b g Z P L r / D U G m a T e f C s ,
log
^10 N 10 13 14 18 23 26 27 9 21 24 17

30. THEORY OF TRICKLE BED MAGNETOHYDRODYNAMICS
IN INHOMOGENEOUS MAGNETIC FIELDS – Potential route to process intensification by I. Iliuta and F. Larachi
for positive magnetic gradients, gas magnetization force amplifies the effect of gravity (macro-gravity) and two-phase pressure drop is reduced
When magnetic gradients are positive, liquid holdup increases with increasing |BdB/dz| because the driving force decreases
G=1.2
kg/m3
G=47
kg/m3
- for negative magnetic gradients, the upward gas magnetization force reduces the effect of gravity (sub or micro-gravity) and two-phase pressure drop increases
When magnetic gradients are negative,
liquid holdup decreases with increasing
|BdB/dz| because the driving force (two-phase pressure drop) increases

31. Elevated levels of magnetic field gradient improve the liquid holdup
and thus the wetting efficiency of the catalyst particle
Because phenol oxidation is liquid-reactant limited,
as catalyst wetting improves the phenol conversion
increases significantly

32. Prediction of HETP for randomly packed tower operation: Integration of aqueous and non-aqueous mass transfer characteristics into one consistent correlation Simon PICHÉ, Stéphane LÉVESQUE, Bernard GRANDJEAN, Faïçal LARACHI Department of chemical engineering & CERPIC Québec, CANADA G1K 7P4 L G S
POLLUTION CONTROL: Particulate removal, SO2, NOX, VOC & TRS scrubbing
WATER PURIFICATION: Ammonia stripping and recovery, VOC stripping
DISTILLATION: Styrene purification (Ethylbenzene - Styrene separation)
(Vacuum or Pressure) Demethanizers (ex: CH4 removal / heavy feedstock)
OBJECTIVE: Build a new, efficient & consistent correlation using
an artificial neural network, dimensionless analysis & data (HETP,
KGaW, KLaW) reconciliation procedure that could predict either HETP
for distillation or kGaW, kLaW, KGaW & KLaW for absorption and stripping
DATABASE: absorption/stripping measurements
2357 distillation measurements

33. General procedure and Results
(1) Testing of ANN correlations developed for aqueous solutions [1] on HETP (non-aqueous solutions)
(2) Extraction of pseudo interfacial areas from HETP and mass transfer coefficients
(with previously developed ANN-kg ) & development of new interfacial area correlation (ANN-aW)
(3) Weights reconciliation on the 6 mass transfer parameters (HETP, aw, kLaw, kGaw, KLaw, KGaw)
General procedure and Results
ANN-kgI
kg (cal)
aw (pseudo)
(5,802 data)
aw (exp)
(325 data)
ANN-aw(3)
ANN-aw(3) & ANN-kgI weights reconciliation
HETP, KLaw, KGaw, kLaw, kGaw, aw (6,127 data)
ANN-awII
ANN-kgII
HETP, Kgaw & kgaw (exp)
ANN-awI & ANN-kgI testing on HETP
m = 76%, s = 100%
[1] Piché, S., Larachi, F. & Grandjean, B., Reconciliation procedure for gas-liquid interfacial area and mass
transfer coefficient in randomly packed towers, Ind. Eng. Chem. Res., 41 (19) (2002)
ANN STRUCTURES: g = G (gas) and L (liquid)
[1] aw/aT = f (ReL, FrL, EoL, wall factor, c) – ANN-awI
kg /(aTDg) = f (Reg, Frg, Scg, c) – ANN-kgI
This work
aw/aT = f (ReL, FrL, EoL, wall factor, c, RSI) - ANN-awII
kg /(aTDg) = f (Reg, Frg, Scg, c) – ANN-kgII
RSI (Relative stability index) = (dsL/dxvol) / sL(mixture)
RSI=0 (aqueous solutions); RSI<>0 (non-aqueous mixtures)
STATISTICAL PERFORMANCE:
HETP (N=2357, m=21%); aW/aT (N=325, m=24%);
kLaW/kGaW (N=1461, m=23%); KLaW/KGaW (N=1984, m=29%)

34. Next Presentation

35. Modelling of Radioactive Tracer Distribution in Bubble Columns
CHEMICAL REACTION ENGINEERING LABORATORY
Modelling of Radioactive Tracer Distribution in Bubble Columns by
Chengyu Mao
Advisor: Prof. M.P. Dudukovic
Prof. P.A. Ramachandran

36. CHEMICAL REACTION ENGINEERING LABORATORY
Many engineering models are available for description of flow, mixing, and transport in bubble columns. However, their accuracy to simulate and predict experimental data needs to be verified.
Liquid Recirculation Model
Recycle with Cross Flow and Dispersion Model (RCFDM)
Single Bubble Class Model (SBCM)
Distributed Bubble Size Model (DBSM)
Two Dimensional Convection with Eddy Diffusion Model
Now there are several engineering models for flow, mixing and transport in bubble columns. And we have built the corresponding experiments to obtain the related parameters for these model. So, we wonder whether these models work well and whether they can simulate and predict the experiment data.
Objective: From the experiments, get the experiment data and the pertinent parameters for the available models.
Using the model, calculate to get the tracer concentration in the bubble column.
Based on the model, simulate the detector responses and compare it with the experiment data, verify the available models.

37. CHEMICAL REACTION ENGINEERING LABORATORY
Single Bubble Class Model (SBCM)
Two Dimensional Convection with Eddy Diffusion Model
Liquid tracer concentration distribution
Method 1
Calculate Cross-Sectional concentration, normalize and compare with data
Method 2
Calculate 2D Response accounting approximately for the attenuation of radiation, normalize and compare with data
Method 3
Calculate 3D Response accounting fully for the attenuation of radiation, normalize and compare with data
(point to the bottom and top of the column) Distributor zone and disengagement zone, they are considered as CSTR.
(Point to the middle part of the picture), here are the fully developed part of the flow, which occupies most of the column. It consists of 4 zones. Gas down flow, gas up flow, liquid down flow and liquid up flow.
At each zone, the concentration is uniform at a given elevation.
According to this compartmentalization, we can get the SBCM equations. (Turn to next slide)

38. Next Presentation

39. M.P. Dudukovic, M.H. Al-Dahhan, P. Spicka
Liquid Maldistribution in Trickle Bed Reactors Experimental and CFD Modeling Study
Nicolas Dromard
Master in Process Engineering, INPL, Nancy, FRANCE
Chemical and Process Engineer, ENSIC, Nancy, FRANCE
M.P. Dudukovic, M.H. Al-Dahhan, P. Spicka
Chemical Reaction Engineering Laboratory, Washington University
St Louis, USA
D. Védrine, J. Bousquet
Centre Européen de Recherche et Technique, TOTALFINAELF
Harfleur, FRANCE

40. Liquid Maldistribution in TBRs
An Experimental Study
How to quantify maldistribution?
Determination of the parameters responsible for maldistribution
A step to CFD modeling
Generate a pseudo random porosity profile ?

41. Next Presentation

42. Multiphase Packed-bed Reactor Modeling
CHEMICAL REACTION ENGINEERING LABORATORY
CREL Annual Meeting, 2002
Jing Guo
Dr. M. H. Al-Dahhan
Multiphase Packed-bed Reactor Modeling
output
input
C0, usL
Xa, C, usL,
Motivation
Scale up of packed bed:
Simulation vs huge amount of experimental work
? How to choose multiple level model for reaction system of interest
Reactor scale
Completely
inactively wetted
Half wetted
Pellet scale
Understand and compare the hierarchy of model for catalytic multiphase packed-bed reactors
Capture the time-dependent reaction features of catalytic wet oxidation in packed beds
Objective
Completely
actively wetted

43. Combination of Reactor and Pellet Scale Model
CHEMICAL REACTION ENGINEERING LABORATORY
Combination of Reactor and Pellet Scale Model
Pellet scale
Wet side
Dry side
Ci,j
C i, N
C i, N-1
C i, j
C i, 2
C i, 1
C i, 0
Reactor Scale
El-Hisnawi Model
Beaudry Model
Reaction
wet oxidation of phenol
over deactivating catalyst
I:reactant component
J:Cell sequence
El-Hisnawi Model
Reactor Axis
Pellet scale
Ul, Ug
Predict axial
concentration
distribution
Beaudry Model
Active site distribution after 110 hours
Refine local effectiveness factor
Predict catalyst local performance

44. Next Presentation

45. Shaibal Roy Muthanna Al-Dahhan
Poster 1
New CT Setup and studies on Gas-Liquid Hold-up in Structured Packing using CT
Shaibal Roy
Muthanna Al-Dahhan 4”
4"
Guide for the source
Plate 1
Plate 2
Main difference between the old and new CT
Resolution Characteristics of new CT setup
Gas Liquid Flow Characteristics (hold-up and pressure drop) in a 12 inch Structured packing

46. A Study of Structured Packing for Solid Catalyzed Gas-Liquid Reaction
Poster 2
A Study of Structured Packing for Solid Catalyzed Gas-Liquid Reaction
Shaibal Roy
Muthanna Al-Dahhan
Motivation
Structured Packing for solid catalyzed gas-liquid reaction provides
Large Volumetric productivity
Lower pressure drop
Excellent mass transfer properties
Higher selectivity (low axial dispersion, short diffusion length scale)
Ease of scale-up

47. Research Objective Research Goal
The overall objective of the proposed study is to develop better understanding
and fundamentally based model for comparison of structured packing (e.g. Monolith and other selected configuration) with conventional reactors for multiphase reactions
Research Goal
Hydrodynamic aspects in structured packing
Apparent Kinetic model with extrudates
And washcoated monolith
Combined effects in overall performance of structured packing for multiphase reactions
Compare with conventional packed bed reactor
Develop model for performance prediction

48. Next Presentation

49. A Non-Invasive Method for Overall Solids Flux Measurements in a Circulating Fluidized Bed (CFB)
Satish Bhusarapu, Pascal Fongarland,
M. H. Al-Dahhan and M. P. Dudukovic’
CREL Annual Meeting
October 24, 2002
Chemical Reaction Engineering Laboratory
Department of Chemical Engineering
St.Louis, MO 63130

50. Challenge : How to measure overall solids mass flux accurately in a CFB ?
Obtain solids velocity and concentration in a standpipe where solids hold up is high
Solids velocity – “time of flight” measurements – track a single radioactive tracer using a two detector set-up
Solids concentration – g-ray line densitometry
Overall solids flux at varying operating conditions

51. Next Presentation

52. Hu-Ping Luo, Muthanna Al-Dahhan
CHEMICAL REACTION ENGINEERING LABORATORY
Photobioreactors for Culturing High Value Microalgae and Cyanobacteria: Experimentation and Modeling
Hu-Ping Luo, Muthanna Al-Dahhan
Chemical Reaction Engineering Laboratory (CREL)
Washington University
Good morning, professors. My name is huping luo. The topic for my presentation is: advanced dia…..
CREL ANNUAL MEETING
October 24, 2002

53. Cells’ growth depends on their movement
CHEMICAL REACTION ENGINEERING LABORATORY
Cells’ growth depends on their movement
Wall or internals
Split Column
In Nonlinear and Dynamic Systems, results are determined by the process
Feeding to the cells is irregular in bioreactors, especially for light
Spiral movements, time scale: 1 s
Light distribution
A cell’s movement in the reactor determines its accessibility to the light, nutrients, the shear stress need to endure, etc.
We are more interesting in the movement of the cells in the reactor. CARPT technique is a very useful tool for us since it reproduce the movement of the radioactive particle in the reactor for very long time, good for statistic analysis, at high frequency, 50 Hz. Since we can analogize the particle’s movement to the cell’s movement, we can use CARPT data straightforward to calculate the growth rate.
Here is a trajectory of the particle for 20 second. That’s not good for analysis. So we extract only one circulation of the particle for analysis. We called it single trajectory.
So far, if we can calculate the light intensity distribution in the reactor, we can obtain the irradiance pattern for each trajectory. Let’s see the following figures
y, cm
radius, r, (cm)
x, cm

54. Dynamic approach for photobioreactor analysis
CHEMICAL REACTION ENGINEERING LABORATORY
Dynamic approach for photobioreactor analysis
Challenge: How to combine the knowledge of:
Physiology of photosynthesis process
Reactor hydrodynamic
Current approaches: use Static kinetic model for bio-reactions, take into account only average light intensity (effects of hydrodynamic are ignored)
Our new approach: combines dynamic kinetic model for bio-reactions with hydrodynamic information via CARPT experimental technique.
Let’s look at a real example. Here I’d like to introduce the three-state growth rate model of photosynthesis. This model use the conception of photosynthetic factory, which is associated with the microstructures in a cell who are responsible for the photosynthesis. A basic assumption is that: PSE has three states: resting state, activated state and inhibited state.
At the resting state (x1), the cell can be stimulated and transferred to the activated state if it captures a photon. it’s light dependent. At the activated state (x2), the cell can be either passing the gained energy to acceptors to start the photosynthesis (respiration processes, also called dark reaction. It’s associated with enzymatic reaction and is light independent) and then return to the open state (x1), or receiving another photon to be inhibited (transferred to x3). At the inhibited state (x3), meanwhile, cells recovered by a complicated and costly repair mechanism (Barber & Andersson, 1992) and return to the open state (x1).
According to this, we can derive the differential equations as following:
X1, x2, x2 is the fraction of the cells in the resting state, and also x2, x3.
This is the summation of the states. It should be unity.
Eq(5) is the growth rate. X is the total number of cells. And please note, only the reaction from x2 to x1 is associated to the growth. Me is a constant. Since if light is too weak, the biomass will decrease. This constant is to take account of these effects.
If the light intensity is constant, we can see, these differential equations is a linear system. It’s very easy to be solved. Here I didn’t solve it, instead, I did some steady state analysis, which can give us some valuable information.
Please stop at my poster, I’ll show you all the details

55. Next Presentation

56. CHEMICAL REACTION ENGINEERING LABORATORY
FLOW PATTERN IMAGING INSIDE A SIMULATED ANAEROBIC DIGESTER USING CARPT AND CT
Washington University Group:
Rebecca Hofmann, Mehul Vesvikar, Rajneesh Varma, Khursheed Karim, Muthanna Al-Dahhan
Oak Ridge National Lab. Group:
Thomas Klasson, Alan Wintenberg, Chuck Alexander, David Depaoli

57. ANIMAL WASTE :Environmental Perspective and motivation for Treatment
CHEMICAL REACTION ENGINEERING LABORATORY
ANIMAL WASTE :Environmental Perspective and motivation for Treatment
More than one billion tons of animal waste generated every year in the USA.
Unsafe and improperly disposed
Surface & groundwater contamination
Ammonia leaching
Methane emission
Odors
Methane = Energy source, hence animal waste = renewable energy source
Biomass has applications of fertilizer and land fill
High failure rate observed in digesters that is attributed to mixing related problems
Effects of hydrodynamics and mixing in anaerobic bioreactors needs investigation.
Objective of The Present Work
To demonstrate the ability of single particle CARPT technique to visualize 3D flow patterns inside a simulated digester.
To determine the gas phase hold-up of the three phase anaerobic system using single source CT.

58. Next Presentation

59. CHEMICAL REACTION ENGINEERING LABORATORY
Modeling of Catalytic Partial Oxidation Reactors R.C. Ramaswamy, P.A. Ramachandran & M.P. Dudukovic CREL Annual Meeting October 24, 2002

60. Catalytic Partial Oxidation of Methane to Syn-gas
CHEMICAL REACTION ENGINEERING LABORATORY
Synthesis Gas (mixture of CO and H2)
Feed stock to chemical process industries
Feed stock to Syn-fuels, H2 for fuel cells
Catalytic Partial Oxidation of Methane to Syn-gas
CH4 & O2 (2:1)
Tin ~773 K
H2/CO < 2;
CO2 & H2O
Texit ~ 1300 K
Exo. rxn. &
Endo. rxn.
A schematic of syn-gas packed bed reactor
Reactions :
Exothermic Combustion Reaction
Endothermic Steam Reforming Reaction
Slightly Exothermic Water Gas Shift Reaction
Mathematical models are required for design,
control and optimization purposes

61. Next Presentation

62. Transport and Capture of Particles in Magnetic Fields
Prakash Kumar
Advisors: Prof. Pratim Biswas, Prof. M. P. Dudukovic
1. Model to predict the particle
trajectories in magnetic fields.
2. Setup to study the particle transport
Magnetic fields.

63. 3. Comparison of theoretical and experimetal results.
4. Effect of magnetic fields on particle
aggregation.
(Magnetic)
Brownian (Friedlander 1964)

64. The End